Front cover: Image of Beijing resulting from the TM/SPOT merge and enhancement. 
Cooperative Studies Between the United States of America 

and the People's Republic of China on Applications of 

Remote Sensing to Surveying and Mapping 

By Donald T. Lauer and Chu Liangcai, Editors 

Contributing Authors: 

USA 

j. Eidenshink 

j. Hood 

D. Meyer 

F. Sadowski 

D. Scholz 

P. Seevers 

PRC 

Zhicheng Qiu 

Wogen Wen 

Lan Huang 

Fengde Liu 

A report of cooperative investigations between the National Bureau of Surveying and Mapping, People's Republic of 

China, and the U.S. Geological Survey, U.S. Department of the Interior, United States of America. 

U.S. GEOLOGICAL SURVEY BULLETIN 2033 
U.S. DEPARTMENT OF THE INTERIOR 

MANUEL LUJAN, Jr., Secretary 

U.S. GEOLOGICAL SURVEY 

Dallas L. Peck, Director 

Any use of trade, product, or firm names in this publication is for 

descriptive purposes only and does not imply endorsement by the 

U.S. Government 

UNITED STATES GOVERNMENT PRINTING OFFICE: 1992 

For sale by 

Book and Open-File Report Sales 

U.S. Geological Survey 

Federal Center, Box 25425 

Denver, CO 80225 

Library of Congress Cataloging in Publication Data 
CONTENTS 

Introduction 1 

Background 2 

Image Mapping 3 

Objectives 3 

Software Developed 3 

Data Sets 4 

Black Hills Test Site 4 

Ningxiang Test Site 5 

Analysis 5 

Black Hills Test Site 5 

Geometric Registration 5 

Image Enhancement 5 

Special Product Generation 6 

Ningxiang Test Site 8 

Geometric Registration 8 

Image Enhancement 8 

Special Product Generation 8 

Thematic Mapping 9 

Objectives 9 

Software Developed 9 

Data Sets 9 

Black Hills Test Site 9 

Ningxiang Test Site 9 

Analysis 10 

Black Hills Test Site 10 

Data Preparation 10 

Class Statistics Development 10 

Postclassification Processing 10 

Accuracy Estimation 11 

Ningxiang Test Site 11 

Data Preparation 11 

Statistics Development 14 

Classification Approach 16 

Postclassification Processing 16 

Accuracy Estimation 16 

Accuracy Assessment 17 

Other Processing 21 

DEM Derivatives 21 

Data Transformations 21 

Conclusions 22 

Principal Components Analysis 21 

Texture Analysis 21 

Beijing TM/SPOT Image Merge 22 

Evaluation of Products 22 

Benefits of the Technical Exchange 23 

Summary and Recommendations for Future Work 24 

References 24 

Contents Ill 
FIGURES 

1. Map showing the location of the Rapid City image map in the Black Hills of South Dakota. 4 

2. Map showing the location of the Ningxiang study area in Hunan Province. 4 

3. Image showing the Ningxiang test site as extracted from bands 5, 4, and 3 of the TM data. 6 

4. Shaded relief image results of the Black Hills area created from digital elevation data. 7 

5. Comparison of the raw image of the Black Hills classification results and the results of smoothing. 12 

6. The NBSM and EDC team comparing the ground truth data with aerial photographs. 13 

7. A shaded relief image of the Ningxiang test site created with merged 1:10,000- and 1:50,000-scale digital terrain model 

data. 13 

8. Map showing the location of the intensive test site located in the TM and SPOT overlap region of the Ningxiang study 

area. 14 

9. Ellipse plots of the 36 spectral cluster classes calculated for TM bands 3, 4, and 5. Ellipses represent one standard deviation about each class mean. 14 

10. An NBSM scientist checking the initial classification results using the VAX 11/780 computer at the EDC. 15 

11. Ellipse plots of the 28 spectral cluster classes calculated using SPOT bands 1, 2, and 3. Ellipses represent one standard 

deviation about each class mean. 15 

12. Ellipse plot for the best TM two-band combination (bands 3 and 4) calculated from spectral values in TM bands 2, 3, 

and 4. 15 

13. Ellipse plot for the best TM two-band combination (bands 4 and 5) calculated from spectral values in TM bands 1, 2, 3, 

4, 5, and 7. 15 

14. An NBSM scientist comparing land cover categories between the cluster map and the ground truth map. 17 

15. Image of principal component one resulting from the processing of the six bands of TM data. 22 

16. Image of Beijing resulting from the TM/SPOT merge and enhancement. 23 

TABLES 

1. Ground truth land cover classes and their digital values. 11 

2. Ground truth land cover classes compared with the final accuracy categories used for each of the classification results. 17 

3. The six original classifications grouped into 10 land cover information classes. 18 

4. Contingency table for TM bands 2, 3, and 4 classification results. 18 

5. Contingency table for TM bands 3, 4, and 5 classification results with five ground truth categories. 19 

6. Contingency table for TM bands 3, 4, and 5 classification results, which were refined using nominal filtering (two-pixel 

region refinement), with five ground truth categories. 19 

7. Contingency table for TM bands 1, 2, 3, 4, 5, and 7 classification results with five ground truth categories. 20 

8. Contingency table for TM principal components classification using the first two components of the six calculated with 

five ground truth categories. 20 1\ 

9. Correlation of K statistics for each classification result to ground truth calculated using the S statistical package [Note 

that each classification result is significantly different from the others]. 21 

IV Contents 
Cooperative Studies Between the United States of America 

and the People's Republic of China on Applications of 

Remote Sensing to Surveying and Mapping 

By Donald T. Lauer and Chu Liangcai, Editors 

Abstract 

A Protocol established between the National 

Bureau of Surveying and Mapping, People's Republic of 

China (PRC) and the U.S. Geological Survey, United 

States of America (US), resulted in the exchange of 

scientific personnel, technical training, and exploration 

of the processing of remotely sensed data. These activities were directed toward the application of remotely 

sensed data to surveying and mapping. Data were 

processed and various products were generated for the 

Black Hills area in the US and the Ningxiang area of the 

PRC. The results of these investigations defined applicable processes in the creation of satellite image maps, 

land use maps, and the use of ancillary data for further 

map enhancements. 

INTRODUCTION 

This report describes the activities and accomplishments of the scientific and technical exchange between 

the National Bureau of Surveying and Mapping (NBSM), 

People's Republic of China (PRC), and the U.S. 

Geological Survey (USGS), U.S. Department of the 

Interior, United States of America (US), under Annex III 

of the Protocol for Scientific and Technical Cooperation 

in Surveying and Mapping Studies. 

On April 16, 1985, officials of the governments of 

the USA and the PRC signed the Protocol to promote 

cooperation in photogrammetry, remote sensing, cartography, geographic information systems (GIS), and other 

Any use of trade, product, industry or firm names is for descriptive purposes 

only and does not imply endorsement by the U.S. Government. 

studies of surveying and mapping. The Protocol specified 

that cooperative activities must include (l)the exchange 

of scientific and technical information through scientists, 

specialists, technical consultants, and delegations; 

(2)research on subjects of mutual interest including the 

design and installation of instruments and equipment; 

(3)the exchange of technical results and other materials 

considered appropriate and necessary for the cooperative 

effort; (4)the joint organization of scientific conferences, 

symposia, and lectures, or attendance at meetings; and 

(5)other forms of cooperative activities that are mutually 

agreed upon. 

The USGS was assigned the responsibility for coordinating the participation of the US in the joint program. 

The NBSM of the PRC was assigned a similar responsibility for Chinese participation. To administer the cooperative activities, a Joint Working Group was established. 

Each country designated a cochairperson to lead the Joint 

Working Group. These individuals consulted with each 

other on a regular basis to review and plan cooperative 

activities and to perform other related duties. The Joint 

Working Group met periodically to review completed 

work and work in progress and to plan future cooperation. 

Scientific and technical cooperation to promote the 

development of remote sensing techniques and their 

application to surveying and mapping is defined in Annex 

III of the Protocol. Objectives in Annex III include 

(l)the development of techniques for geometric and 

radiometric rectification of remotely sensed data; (2)the 

application of remotely sensed data to map revision; 

(3)applications of remotely sensed data to thematic 

mapping using enhancement and classification techniques; and ( 4 )the integration of remotely sensed data 

from various sources with other types of data for land use 

mapping and related applications. 

Introduction 
BACKGROUND 

To carry out the objectives of Annex III, remotely 

sensed and ancillary data collected over study sites within 

each country were used. The NBSM chose the Ningxiang 

area in Hunan Province, and the USGS selected the Black 

Hills area in the State of South Dakota. These sites were 

selected because preliminary experimental work already 

had been completed and aerial photographs were available 

or soon would be. Both countries immediately began to 

acquire remotely sensed data from the American Landsat 

and the French Satellite Pour !'Observation de Ia Terre 

(SPOT). In both cases, the necessary ancillary maps and 

supporting data were available. 

The cooperative technical exchange began when the 

USGS sent two remote sensing experts to China for 3 weeks in 

late October 1985. The Americans presented a series of 

lectures on the applications of remotely sensed data to surveying and mapping. Topics were remote sensing concepts, characteristics of remote sensing satellite systems, visual 

interpretation techniques~ digital data processing and analysis 

techniques, statistical sampling and accuracy assessment, the 

role of remote sensing in GIS, and applications case studies. 

The lectures emphasized digital data processing techniques for 

preprocessing, enhancement, classification, and product generation. Detailed discussions of specific software algorithms and 

their speed, efficiency, and reliability also were presented 

during the lectures. Chinese experts at the Research Institute of 

Surveying and Mapping in Beijing gave briefings and demonstrations to the American scientists on NBSM computer 

systems, image processing software, and other topics related to 

the project. Following these lectures, the USGS and NBSM 

scientific staff continued technical consultations on the requirements for a comprehensive image processing system needed by 

theNBSM. 

In February 1986, the NBSM sent two experts to the 

US for 3 weeks to study the applications of remotely sensed 

data to surveying and mapping in the USGS. Two weeks 

were spent at the USGS's Earth Resources Observation 

Systems (EROS) Data Center (EDC) in Sioux Falls, South 

Dakota, and 1 week at the USGS headquarters in Reston, 

Virginia. The NBSM scientists became familiar with USGS 

equipment, facilities, and operations during this visit. They 

also developed a detailed work plan to be undertaken later 

in the year. 

In the summer of 1986, the NBSM sent two experts to 

the US for 3 months to perform studies and to be trained in 

image processing applied to specific surveying and mapping 

problems. During this visit, a comprehensive data set for 

the Black Hills study site was used for training, demonstrations, and selected studies. The work emphasized applications of digital image processing to surveying and mapping, 

particularly for making image maps, map revisions, and 

2 Applications of Remote Sensing to Surveying and Mapping 

thematic maps. Also included was training on image 

processing procedures from the calibration of raw data to 

the production of final images. The digital image processing procedures applied to the data set included radiometric 

calibration, destriping, atmospheric correction, control point 

selection, geometric registration, resampling, mosaicking, 

contrast stretching, spatial filtering, band ratioing, training 

site selection, classification, postclassification refinement, 

shaded relief enhancements, and the design of products. 

Work by this team used primarily Landsat thematic mapper 

(TM) and digital terrain data. The NBSM experts gained an 

understanding of the data processing requirements and 

experience in analytic methods for producing image and 

thematic maps. 

The Chinese scientists and their American counterparts spent a week at the Black Hills study site. The visit 

to the Black Hills area involved the field checking of 

several multiband combinations of TM data that were 

determined to be the most desirable for discriminating 

geologic, forest, and agricultural features. In addition, the 

results of the land cover classification of TM bands 5, 4, 

and 3 were evaluated. The team visited five intensive field 

sites that encompassed diverse land cover and topography. 

Field observations confirmed that the digital image 

processing procedures were applied successfully to the 

data set. 

Most of the time during this mid-summer work 

session was spent at the EDC. However, team members 

also visited other leading remote sensing institutes and 

facilities in the US: the USGS headquarters in Reston, 

Virginia; the USGS Western Mapping Center in Menlo 

Park, California; the USGS Field Center in Flagstaff, 

Arizona; the National Aeronautics and Space 

Administration (NASA) Jet Propulsion Laboratory in 

Pasadena, California; the NASA Ames Research Center in 

Moffett Field, California; the NASA Goddard Space Flight 

Center in Greenbelt, Maryland; the Environmental 

Research Institute of Michigan in Ann Arbor, Michigan; 

and the Earth Observation Satellite (EOSAT) Company in 

Lanham, Maryland. 

Two American scientists, in tum, visited the NBSM's 

Research Institute of Surveying and Mapping in Beijing 

from mid-November to mid-December 1986 to conduct 

joint research using NBSM equipment and facilities and to 

carry out technical consultations. The two American scientists and the two NBSM experts who had recently made the 

trip to the USA collaborated to present to the NBSM staff a 

detailed description of the analytic procedures that were 

applied to the Black Hills data set. They displayed intermediate and final results of the joint work. The American 

scientists also provided technical assistance on software 

and hardware systems design, advice on alternative 

approaches to problem solving using different kinds of 
software, and guidance on technique development and 

procedures for generating products. During their visit to 

China, the Americans visited the Cartographic Publishing 

House and the Remote Sensing Laboratory at Beijing 

University and the Guangdong Land and Development 

Department in Guangzhou. 

The cooperative work between the USGS and the 

NBSM continued in 1987 and 1988. Satellite image data 

had been acquired over both the Ningxiang and Black Hills 

study sites from the French satellite SPOT. Preprocessing 

of these data was carried out independently by Chinese and 

American scientists for their respective study sites. In the 

fall of 1987 and spring of 1988, two American scientists 

spent 4 weeks in Beijing and two Chinese scientists spent 6 

weeks at the EDC. The intent of these visits was to produce 

a 1: 100,000-scale image map of the Rapid City quadrangle 

in the Black Hills study site using Landsat TM data 

acquired in 1985 and a 1:50,000-scale image map of a part 

of the Ningxiang study site using SPOT data acquired in 

1986. In addition, the scientists performed thematic classifications of predominant land cover types and cultural 

features occurring in both study sites. 

Photographic products generated during these reciprocal visits included image data merged with shaded relief 

data in those cases where digital elevation data were available. Investigations were initiated to determine the effects 

of relief displacement on image maps. Map revision procedures were investigated using both digital image processing 

and manual image interpretation. In addition, the team 

tested image processing and analytic software capabilities 

using both USGS and NBSM computers. 

During the fall of 1988 two American scientists 

visited the NBSM to review software development and the 

initial land cover classification in the Ningxiang test site. 

Ground truth data collected on the test site were reviewed. 

During the spring of 1989, two Chinese scientists visited the 

EDC to classify land cover of the Ningxiang test site using 

Landsat TM and SPOT data. Digital elevation model 

(DEM) data and ground truth data were used to refine the 

land cover classification. The team also assessed classification accuracy during this visit. The remainder of 1989 was 

spent compiling results of the previous 4 years of cooperative research. Procedures and accomplishments were documented and compiled for this report. 

IMAGE MAPPING 

Objectives 

The main objective of the image mapping phase of the 

Protocol was to develop a comprehensive image map 

production facility at the NBSM. This effort began with 

NBSM staff coming to the EDC to study the USGS image 

mapping system. The Rapid City image map was generated 

as part of this review. After this review was completed, 

EDC staff traveled to Beijing to support the NBSM in transferring the important elements of the EDC system to the 

Beijing facility. NBSM staff then modified the system to 

meet their requirements. EDC staff later assisted the 

NBSM with system performance assessment by reworking 

elements of the Rapid City image map on the NBSM 

system and comparing the results with the map generated at 

the EDC. When the system was validated, work began on 

the Ningxiang image map, which was generated using both 

the NBSM and EDC systems. In this manner, the two 

systems demonstrated the consistency of the products that 

they generated, laying the foundation for applications 

research to be conducted jointly at both facilities. 

Software Developed 

The NBSM system was derived from the GEOREG 

software package developed at the EDC. Called the Digital 

Image Mapping Package (DIMP), the system operated on a 

Prime computer with a Pericolor display system. DIMP is 

functionally equivalent to GEOREG with a few modifications. 

Five "rough" control points, rather than four, are used to derive 

the first-order transformation coefficients, which initialize the 

transformation refinement process. The software can model 

distortion geometry with polynomials of up to the fifth order, 

although second order is used for multispectral scanner (MSS), 

TM, and SPOT data. The projection software models 

geographic, Universal Transverse Mercator (UTM), and 

Gauss-Kruger projections. Resampling includes nearest 

neighbor, bilinear, and cubic convolution. Below is a brief 

description of the DIMP modules: 

XIN 

REGMAP1 

REGMAP2 

LBXYGK 

LBXYUT 

TRCOEF 

GEOMP 

VERIFY 

STIEPT 

REGIS 

Creates a file for parameter storage and file 

management. 

Computes rough transformation coefficients 

for image-to-map base registration. 

Selects and edits control points. 

Converts geographic coordinates into GaussKruger projection. 

Converts geographic coordinates into UTM 

projection. 

Computes high-order registration coefficients 

for image-to-map base registration. 

Resamples for image-to-map base registration. 

Verifies geometrically corrected products. 

Computes rough transformation coefficients 

for image-to-image registration. 

Automatically correlates for image control 

selection. 

Applications of Remote Sensing to Surveying and Mapping 3 
GEOIMI 

GEOMPI 

TRCOPT 

CTPME 

CCNB 

INLUT 

CUTF 

ZERFIL 

MOSAIC 

HPF 

Data Sets 

Computes the high-order registration coeffi 

cients for image-to-image registration. 

Resamples for image-to-image registration. 

Calculates geodetic coordinates for tie points. 

Merges tie points and control points for 

mosaic king. 

Histograms overlap area for adjacent scenes 

in a mosaic. 

Adjusts radiometry for reducing mosaic seam 

artifacts. 

Cuts images along seams for mosaicking. 

Creates the mosaic file. 

Performs the mosaicking. 

Performs high-pass filtering for image 

enhancement. 

Black Hills Test Site 

The Black Hills test site encompasses Rapid City and 

a large area including most of the Black Hills National 

Forest and surrounding rangeland and agricultural land. 

The landscape is mountainous, with an overall topographic 

relief of approximately 800 to 1,000 meters. The area is 

sparsely populated and has only a few towns and ranches 

and a transportation network that is well developed but not 

complex. High-altitude aerial photographs were available 

Figure 1. Map showing the location of the Rapid City 

image map in the Black Hills of South Dakota. 

4 Applications of Remote Sensing to Surveying and Mapping 

of the Black Hills area and were acquired during flights in 

September 1981 and September 1982, under the auspices of 

the National High-Altitude Photography Program. Other 

map and digital data sources also were available for this 

area. The Black Hills region is a unique geological and 

ecological area that has been studied extensively (fig. 1). 

The surrounding agricultural land is mostly dryland wheat 

with small fields of irrigated cropland adjacent to the Belle 

Fourche River and Rapid Creek. The grassland is used for 

livestock production. 

As a result of studies of the Black Hills area by 

remote sensing and GIS scientists, several data sets are 

available. One of the most important is a high-resolution 

DEM data set. The data set is a mosaic of a large number of 

USGS 1:24,000-scale DEMs. The data set is complete for 

the Black Hills, but incomplete for some of the agricultural 

and rangeland area surrounding the Black Hills. Lower 

resolution digital elevation data from two USGS 

1 :250,000-scale quadrangles were available for the entire 

study area and were used to supplement the data in areas 

where the high-resolution DEM data did not exist. 

The primary data used for the image mapping activity 

in the Black Hills study area were two Landsat TM images 

acquired on August 9, 1984. The images (identification nos. 

Y5016117054XO and Y5016117060XO) were consecutive 

scenes in the same orbit path (path/row 33/29 and 33/30). 

The consecutive sequence of the scenes was advantageous 

!'llJ SPOT Data 

L\\IJTM Data 

1!1!1! Overlap Region 

Figure 2. Map showing the location of the Ningxiang 

study area in Hunan Province. 
because the radiometry of the scenes was identical and no 

special enhancements or processing were necessary when the 

scenes were mosaicked for the study area. 

The final image map of Rapid City published by 

the USGS conformed to USGS standards and included 

map collar information. Prior to publishing, an intermediate photographic image map product was made for 

review by the cooperators. The intermediate map 

included collar information such as scale bar, location 

diagram, 10,000-meter grid lines, descriptive text, and latitude and longitude location. Portions of the text were in 

both English and Chinese. 

Ningxiang Test Site 

The Ningxiang study area (fig. 2) involved the 

Ningxiang and the Mei Tan Ba 1 :50,000-scale quadrangles. 

The Ningxiang area is primarily an agricultural region 

composed of both irrigated and nonirrigated cropland. It has 

rolling topography with elevation differences of 200 to 300 

meters. There is some forest land at high elevations. The 

area is densely populated and has many towns and villages 

and a complex transportation network. The NBSM recently 

obtained high-altitude aerial photographs of the study site. 

The aerial photographs were acquired by a jet aircraft at 

approximate ly 5,100 meters above the terrain. The 

photographs obtained during this flight, as well as existing 

topographic and thematic maps, were available. TM data 

acquired August 25, 1987, covered approximately the upper 

three-fourths of the Ningxiang quadrangle and the same 

portion of the. east half of the Mei Tan Ba quadrangle. These 

data were geometrically corrected and registered to a map 

base. Restoration was used as the resampling procedure to 

derive 20-meter resolution data. Figure 3 shows the 

Ningxiang test site as extracted from bands 5, 4, and 3 of the 

TM data. Tite SPOT data acquired on September 21, 1986, 

for the Ningxiang test site covered approximately the upper 

three-fourths of the Mei Tan Ba quadrangle and the same 

portion of the west half of the Ningxiang quadrangle. These 

data were geometrically corrected and registered to a map 

base. Restoration was used as the resampling procedure to 

derive 20-meter resolution data. 

Topographic data for the Mei Tan Ba quadrangle were 

generated by digitizing linework from 1: 1 0,000-scale maps 

of the quadrangle. This linework was digitized in 

ARC/INFO by NBSM personnel, brought to the EDC, and 

converted to raster format for merging with the spectral data. 

The topographic data were used to create a color-coded, 

shaded relief map of the Mei Tan Ba quadrangle. 

A customized map collar was prepared for each of the 

quadrangles. Line-work was developed on the Intergraph 

system and consisted of a collar and graticules of a 2,000-

meter grid in a projection of Chinese origin. English and 

Chinese text were prepared for labeling each quadrangle. 

The TM and SPOT data were merged to produce the 

image map for each quadrangle. Histogram matching was 

used to spectrally combine the two data sources. These 

combined images were further enhanced by filtering and 

contrast stretching the data. 

Analysis 

Black Hills Test Site 

Geometric Registration 

To demonstrate EDC geometric registration and digital 

mosaicking capabilities, a digital mosaic over the Rapid' 

City, South Dakota, 1: 1 00,000-scale quadrangle was 

prepared from the two Landsat TM images. Approximately 

25 ground control points were selected from 1 :24,000-scale 

topographic maps for each of the two images. The ground 

control points were reprojected to the UTM projection and 

used in the Large Area Mosaicking System (LAMS). 

LAMS is a subset of the Land Analysis System (LAS) image 

processing system and makes mosaics of digital data sets. 

Digital mosaicking involves adjusting the geometric and 

radiometric discontinuities between images along mosaic 

seams. The two images were resampled to 25-meter resolution using cubic convolution resampling. Because the two 

images were consecutive scenes along the sa~e orbital path, 

no radiometric adjustments were required. Geometric verification showed a total root-mean-square error (RMSE) of 

2.48 pixels with a line and sample RMSE of 1.49 and 1.98 

pixels, respectively. The foregoing table reports the geometric verification results for the two images. 

Q..u..w.u.1 ~ B..efud Disolacemeot 

Point Line Sample Line Sample Line Sample 

433 290 1.0 4.0 0.48 2.76 

2 318 337 -1.0 4.0 0.36 2.06 

3 276 1549 1.0 1.0 0.00 0.00 

4 307 1423 0.0 1.0 0.05 0.30 

5 1101 2399 0.0 0.0 -0.02 0.10 

6 1363 1511 -2.0 1.0 0.24 1.39 

7 1820 1569 -2.0 1.0 0.22 1.26 

8 2241 1509 2.0 0.0 0.28 1.63 

9 1412 816 0.0 3.0 0.53 3.04 

10 1253 2161 0.0 1.0 0.00 0.04 

11 1296 2403 2.0 2.0 -0.02 -0.07 

12 420 2595 1.0 1.0 0.00 0.00 

13 2064 2870 3.0 0.0 -0.01 0.00 

Image Enhancement 

To enhance the local information in homogeneous 

Applications of Remote Sensing to Surveying and Mapping 5 
Figure3. Image showing the Ningxiang test site as extracted from bands 5, 4, and 3 of the TM data. 

areas and to increase the effectiveness of subsequent contrast 

enhancements, a large-kernel (151- by 151-pixels), high-pass 

spatial filter was applied to the digital mosaic. A smallkernel (5- by 5-pixels), high-pass filter was then applied to 

enhance the high-frequency information in the image. To 

improve the apparent contrast in the final mosaic, a piece .. 

wise linear contrast enhancement was applied to the filtered 

images. The following break points were selected from the 

cumulative histograms for each of the three bands: 

Band 4 (red) From: 20 62 87 113 205 254 255 

To: 0 45 147 215 245 245 255 

Band 3 (green) From: 18 41 62 83 175 254 255 

To: 0 30 135 200 245 245 255 

Band 2 (blue) From: 33 46 58 70 122 254 255 

To: 0 25 125 190 245 245 255 

The NBSM transferred film transparencies of the final 

digital mosaic with digital map collar information to the 

USGS for reproduction in lithographic form. A copy of the 

corresponding topographic map was printed on the reverse 

side of the final product. 

6 Applications of Remote Sensing to Surveying and Mapping 

Special Product Generation 

Areas of joint interest were the development of shaded 

relief images derived from DEM data and images in which 

other digital data are draped over the shaded relief image(s). 

Several experimental shaded relief images were produced from 

the high-resolution DEM and TM data using the LAS software. 

The LAS software uses a variety of illumination angle 

and azimuth directions to produce shaded relief images. The 

objective of the work was to demonstrate how different illumination conditions would affect the products. A series of 

nine 512- by 512-pixel images were produced using only the 

DEM data. The images illustrate how variation in illumination conditions can be used to enhance geologic faults and 

lineaments, which are very extensive in the Black Hills. The 

images also demonstrate how the selection of the correct illumination angle can emphasize slope and aspect (see fig.4). 

A series of 512- by 512-pixel images of satellite data 

draped over the shaded relief image was also produced. The 

composite of TM bands 4, 3, and 2 depicted the scene as a 

false-color image. Two methods of draping data over the 

shaded relief image were tested. The first approach was to 

merge each of the three bands of the false-color composite 
Figure 4. Shaded relief image results of the Black Hills area created from digital elevation data. 

Applications of Remote Sensing to Surveying and Mapping 7 
separately with a copy of the shaded relief image and then 

recombine the red-green-blue (RGB) enhanced bands for the 

final product. The second approach was to transform the 

three RGB bands of the satellite image to hue-intensity-saturation (HIS) data, substitute the shaded relief information for 

the intensity band of the HIS data, and retransform the three 

bands back to RGB. The results are similar in appearance, 

but the HIS approach has more versatility for enhancing 

image features in the final color representations. The results 

also demonstrated how shaded relief images can make satellite data easier to interpret. All of the images were produced 

as film transparencies for duplication. 

Ningxiang Test Site 

Geometric Registration 

A TM/SPOT digital mosaic was prepared for the 

Ningxiang test site. Geometric control was selected by the 

NBSM staff using control point selection software developed 

on NBSM computer systems. The control points were transferred to EDC's LAMS for digital mosaicking. Both images 

were transformed using cubic convolution resampling to the 

Gauss-Kruger projection at 20-meter resolution. 

To ensure the accuracy of the control point selection, 

selected areas were digitally magnified. A small, moveable 

window on the display screen was positioned over the 

selected control point. The window was then magnified 10 

times, resulting in a control point selection error of less than 

one pixel. Control points were selected where ground 

features showed well-defined intersections of roads, field 

boundaries, rivers and roads, and so on. A total of 15 control 

points were selected on the SPOT image. Geometric verification showed an RMSE of 0. 71 pixels, with a line and 

sample RMSE of 0.57 and 0.43 pixels respectively. 

SPOT Residual Data 

A total of 17 control points were selected for the TM 

image. An RMSE of 0.83 pixels was shown by geometric 

verification, with a line and sample RMSE of 0.58 and 0.83 

pixels respectively. 

TM Residual Data 

Q.u..tu.u1 ~ 

Point Line Sample Line Sample 

893 533 0.06 -0.29 

2 768 685 0.25 -0.55 

3 549 790 1.04 0.74 

4 545 1159 0.03 0.91 

5 870 866 -0.23 -0.30 

6 781 107 -0.03 0.43 

7 803 86 0.45 0.78 

8 634 337 -0.75 -1.44 

9 515 1999 0.67 -0.40 

10 550 2016 0.60 -0.46 

11 827 2003 -0.63 0.50 

12 586 1589 0.23 0.24 

13 614 1484 -0.34 -0.30 

14 299 1737 -1.01 0.13 

15 194 74 -0.56 0.16 

16 464 206 -0.49 -0.09 

17 43 75 0.66 -0.16 

Image Enhancement 

To avoid enhancing the resolution differences 

between the TM and SPOT portions of the digital mosaic, 

no spatial filters were applied to the final product. The 

following piecewise linear stretch was applied to improve 

the overall spectral contrast of the image. 

Red From: 0 7 38 89 144 171 

To: 0 5 30 145 215 255 

Q.u..tu.u1 ~ Gr~n From: 0 10 15 28 64 103 

Point Line Sample Line Sample 

1464 775 0.26 -0.02 

2 1292 1007 -0.61 -0.14 

3 1128 791 0.05 -0.15 

4 1230 549 0.69 0.06 

5 1283 172 0.40 -0.33 

6 1271 311 -1.06 0.40 

7 903 795 0.24 -0.10 

8 1007 960 0.25 0.17 

9 687 1109 -0.26 -1.06 

10 1031 1419 0.57 -0.33 

11 908 1318 -0.52 0.89 

q 453 902 0.96 0.44 

13 368 846 -0.77 0.04 

14 155 71 -0.03 -0.02 

15 67 683 0.02 -0.07 

8 Applications of Remote Sensing to Surveying and Mapping 

To: 0 0 25 130 190 255 

Blue From: 0 21 25 35 52 73 

To: 0 0 20 120 180 255 

Special Product Generation 

Image maps of the two 1 :50,000-scale quadrangles 

(Mei Tan Ba and Ningxiang) were produced, and a map 

collar with Gauss-Kruger coordinates and Chinese map 

titles and labels were photographically reproduced. This 

product represented the first multisensor mosaic produced 

by the USGS. 

Topographic data for the Mei Tan Ba quadrangle 

were generated from digitized hypsography from 

1: 10,000-scale maps provided by the NBSM. The data 

were converted to raster format in 20-meter cells. A shaded 
relief image was produced from the DEM data using LAS 

software with a simulated sun location 10° above the 

eastern horizon. The original elevation values were then 

grouped into representative ranges and draped over the 

shaded relief image to produce an enhanced color image. 

Custom map collar information was merged with the image, 

and the image was rendered as a color ink-jet plot. 

THEMATIC MAPPING 

Objectives 

Both the US and PRC need land cover and land use 

information. The size of the countries and the diversity of 

traditional land cover and land use information is a motivation for using high-resolution satellite images to produce 

new information or update existing information. 

Research has shown that accurate and detailed information can be extracted from the spectral information in 

Landsat TM data and SPOT data. The success of such an 

effort can often depend on the chosen data classification 

approach. Important factors, including the availability of 

existing ground information, the economics of gathering 

new ground information, the complexity of the land being 

mapped, the classification framework, and the availability 

of supporting data such as topography, often dictate classification decisions. The objective of this investigation was 

to evaluate classification approaches. Several elements of 

classification were addressed, including selection of training data, data clustering, and postclassification refinement. 

Another objective was to help establish priorities for 

software development and implementation on the NBSM 

computer system. Developing a system for classification 

can be difficult. The identification of the key approaches to 

classification and software performance issues were, therefore, important. 

Software Developed 

The Maximum Likelihood Decision Rule (MLDR) has 

a high classification accuracy and was used in some cases for 

this investigation. However, MLDR is a supervised classification and requires that all desired classes be present in the 

image. To get a better representation of classes in the image, 

unsupervised classification software, known as the Iterative 

Self-organizing Clustering Program, was added to the 

system. In addition, the NBSM developed editing functions 

to correct errors found in the classified image. These functions used a cursor to point to the incorrect classification for 

relabeling. To use DEMs to refine the classification results, 

the NBSM developed additional functions to calculate slope 

and aspect images. These functions were then used to 

modify the classifications where appropriate. 

Data Sets 

Black Hills Test Site 

TM data for the Black Hills test site were acquired on 

August 9, 1984. The two images were registered to a map 

base in the UTM projection. The data were resampled to 

20-meter pixels using the cubic convolution procedure. 

Ningxiang Test Site 

The Ningxiang test site involved the Ningxiang and 

Mei Tan Ba 1 :50,000-scale quadrangles. TM data, acquired 

August 25, 1987, covered approximately the upper threefourths of the Ningxiang quadrangle and the same portion 

of the east half of the Mei Tan Ba quadrangle. These data 

were geometrically corrected a second time for the thematic 

mapping work done during early 1989. The data were 

registered to a map base and resampled to 20-meter resolution using the nearest neighbor interpolation method. 

The SPOT data for the Ningxiang test site were 

acquired September 21, 1986, and covered approximately 

the upper three-fourths of the Mei Tan Ba quadrangle and 

the same portion of the west half of the Ningxiang quadrangle. These data were geometrically corrected and registered 

to a map base. For the early 1989 classification work, the 

data were resampled to 20-meter resolution using nearest 

neighbor interpolation to match the resampling procedure of 

the TM data. 

Topographic data for the classification work were a 

combination of the manually digitized 1:10,000-scale data 

for the Mei Tan Ba quadrangle and the 1 :50,000-scale data 

for the Ningxiang quadrangle. The linework was digitized 

by the NBSM in ARC/INFO and was brought to the EDC. 

The registered data were merged, and a specially designed 

spatial filter was used to smooth the transition area between 

the two scales. These data were used to calculate aspect 

and slope images for further analysis. 

The NBSM made a land cover map for the test site by 

interpreting 1: 1 0,000-scale color-infrared aerial 

photographs. The map was digitized in ARC/INFO, 

brought to the EDC, and converted to raster format for 

subsequent analysis. There were 19 Level III land use 

classes. A number of attribute errors were discovered in the 

data that required correction before the data could be used 

in the accuracy assessment. 

Because the test area that compared TM and SPOT 

spectral classification accuracies was a combination of 

portions of the two original quadrangles, new linework was 

Applications of Remote Sensing to Surveying and Mapping 9 
constructed for the final products. This linework included a 

map collar, a graticule of a 2,000-meter grid in a projection 

of Chinese origin, and a location map showing what 

portions of the two quadrangles were involved in the test 

area. A similar set of linework was also constructed for the 

results of the TM scene analysis. 

Analysis 

Black Hills Test Site 

Data Preparation 

The primary data sources for the thematic mapping of 

the Black Hills test site were the two Landsat TM scenes 

acquired consecutively on August 9, 1984. These data had 

been geometrically registered by NBSM scientists during the 

1986 visit to the EDC. The data were resampled to 30-meter 

cells (to match the spatial resolution of the existing DEM data 

set) and mosaicked. Note that the 30-meter Landsat data 

were not used in the image mapping process. TM bands 2, 3, 

and 4 were chosen for the classification process. 

The high-resolution (30-meter) DEM data were 

available and used for postclassification refinement. 

Slope, aspect, and elevation images were derived from the 

DEMdata. 

Existing high-altitude aerial photographs of a portion 

of the study area, which had been acquired in 1983, were 

obtained for use as ground truth data and to aid in selecting 

training sites. 

An image of the 1 :2,000,000-scale digital line graph 

transportation network also was used as an overlay to locate 

ground sites during the field verification process. 

Class Statistics Development 

The thematic mapping effort for the Black Hills test 

site was conducted in 1986. At this time, the only realistic 

approach to gathering training statistics using LAS software 

was the use of an unsupervised method. Methods for 

extracting individual, irregularly shaped training sites were 

still under development. 

The method that was used for selecting the training 

sites was a modified cluster block approach. The process 

involved selecting eight blocks, each 200 lines by 200 

samples. The eight blocks corresponded to areas with existing aerial photographs. The blocks represented the existing 

land cover classes within the Black Hills and surrounding 

region. The eight blocks were mosaicked into one image, 

400 lines by 800 samples, and input to the ISOCLASS clustering algorithm. 

The ISOCLASS algorithm adjusts the parameters that 

control the number of clusters, the standard deviation limits, 

and the distance between cluster means. The first clustering 

attempt used the default parameters. The default number of 

1 0 Applications of Remote Sensing to Surveying and Mapping 

classes was 16. The first clustering test was compared with 

the aerial photographs and showed that more spectral 

classes were desirable. A second attempt was made 

requesting 36 spectral classes. 

Analysis of the statistical information provided by 

the ISOCLASS algorithm showed good separation of 

classes. The information classes were labeled using the 

aerial photographs as ground information. The labeling 

process provided an opportunity to discuss and formulate a 

classification framework that was suitable for the data. 

Eight information classes were identified: barren/disturbed, 

water, grassland, irrigated cropland/shrubs, nonirrigated 

cropland, conifer forest, mixed conifer/deciduous forest, 

and deciduous forest. 

Several other clustering attempts used variations of 

primary parameters to demonstrate the effects of these 

manipulations. Still other clustering attempts used an unsupervised approach. Random samples of pixels were chosen 

and input to the ISOCLASS algorithm. The parameters and 

sample sizes were varied to better understand these variables. 

The results of the modified cluster block approach 

were chosen for use in the classification process. The 

statistical file generated by the ISOCLASS algorithm 

became a part of a maximum likelihood and a minimum 

distance classifier. The entire Black Hills test site was 

classified. Comparison of the results indicated a high 

degree of similarity. A comparison of processing times 

showed very little difference. The conclusion was to use the 

easier method at the NBSM. 

Postclassification Processing 

Postclassification processing uses auxiliary information to refine the information classes. The auxiliary information is usually in the form of images derived from other 

data sources. Typical auxiliary data include soils classes, 

slope, aspect, and elevation. Logical formulations and 

linear models incorporating the auxiliary data are used to 

describe an information class. For instance, logic dictates 

that cropland should not exist on a slope greater than 30 

percent. If a comparison of the information classes from a 

spectral classification with slope information identifies the 

existence of cropland on a steep slope, then the data in the 

spectral classification should be redefined as barren land. 

The Black Hills test site is an area where topographic 

data are very useful in a postclassification process. The 

geologic structure of the Black Hills is a dome that rises 

significantly from the surrounding land. The forest dominates the steep slopes in the higher elevations. The land 

surrounding the dome is reasonably flat and used for cropland and grazing. 

The spectral classification of the Black Hills test site 

resulted in the definition of an irrigated cropland/shrub class. 

This mixed class was a result of the inability to separate 
shrubs in the valleys within the forest from areas of irrigated 

agriculture in the area surrounding the forest. Using the 

topographic data in a postclassification process allowed the 

separation of this spectral class into two information classes. 

A model formulation redeftned irrigated cropland occurring 

at elevations higher than 1,500 meters as shrubs. 

Formulations that are made using sound logic and appropriate auxiliary data can be useful in increasing the accuracy of 

spectral classifications. 

Another postprocessing technique that was used to 

improve the accuracy of the classification was smoothing 

or area filtering. Often spectral classification produces 

"noisy" results. The noise is most evident when it appears 

in what should be a homogeneous region such as a large 

agricultural field. The noise can be removed by using 

smoothing filters on the data. One common approach is to 

move a 3- by 3-pixel, or larger, filter over an image. A 

tally is made of the classes occurring in the window. Rules 

are usually predefined that direct the level of smoothing to 

occur. The simplest rule is to change the center pixel to the 

class value that dominates the window, with priorities 

defined for situations with no clearly dominant class. 

Other variations of the rules can include changing only the 

center pixel if a specific surrounding class exceeds a 

threshold value, or never changing the center pixel if it is 

the water class. 

The results of this type of smoothing can vary dramatically. If care is not taken, the smoothing also can reduce 

the accuracy of a classification. Several methods were 

tested using a subset of the Black Hills test site. Each 

method was evaluated for the effects it appeared to have on 

the data. Figure 5 illustrates the results of some of the 

smoothing processes. 

Another technique that can be used to remove noise is 

area filtering. Area ftltering is a process in which homogeneous polygons or groups of contiguous pixels of the same 

class that do not meet a size threshold are removed by 

merging them with surrounding classes. For instance, a 

group of fewer than 10 pixels can be merged into adjacent 

classes. There are rules that can be used to guide the area 

filtering. A common application would be to establish a 

special threshold for each class. For example, small water 

bodies could be protected by setting a high threshold value. 

Control over class merging can be applied by defining 

which classes can be merged with others. 

Several procedures using this technique were evaluated. This method is most useful if the size of an area is an 

important criteria in a land management situation. 

Accuracy Estimation 

The accuracy of the classification was not verified. 

During the testing of classification methods, most of the 

accuracy verification was done with the aerial photographs. 

The areas used in the cluster block analysis and other areas 

where photographs existed were used in this process. A 5-

day field trip was made to the Black Hills test site to visit 

several ground sites. The sites were representative of the 

different land use and land cover types in the test site. 

Color plots of the classification results were produced at 

1 :250,000-scale. The accuracy was judged qualitatively by 

comparing the classification results with a visual inspection 

at the selected ground sites. In general, the classification 

was more accurate in the forested regions in the Black Hills 

than in the surrounding agricultural areas. Some of the 

inaccuracy was because the dates of the images used in the 

study (August) did not optimally depict agriculture in this 

region. 

Ningxiang Test Site 

Data Preparation 

NBSM staff brought to the EDC in March 1989 

geometrically corrected and registered TM data (bands 1, 2, 

3, 4, 5, and 7) at 20-meter resolution using nearest neighbor 

resampling and digital raster ground truth data at 10- and 20-

meter resolution. The ground truth data had been photointerpreted from 1: 1 0,000-scale color-infrared aerial photographs 

(September 1986), digitized by the NBSM's GIS group using 

their Land Information System, and reformatted to 

ARC/INFO format. These ground truth data contained 19 

Level III land use classes (table 1). The NBSM scientists 

also brought digital terrain data for the Ningxiang quadrangle 

that had been digitized manually from a 1:50,000-scale map. 

These data also were geometrically corrected and registered 

at 20 meters to the Chinese metric projection similar to the 

UTM. Resampling of the SPOT data ensured that any 

differences in classification performance between the TM 

and SPOT classifications were not due to different preprocessing techniques. The SPOT data that had been registered 

at the EDC in 1988, using cubic convolution resampling, 

were reprocessed in March 1989 using the nearest neighbor 

method of resampling. 

Table 1. Ground truth land cover classes and their 

digital values. 

11 Irrigated 

Original data 

Pixel value & name 

41 Natural Grasslands 

12 Dryland Agriculture 51 Cities & Towns 

13 Irrigated Agriculture 52 Rural Residential 

21 Fruit Trees 

22 Mulberry Trees 

23 Tea 

24 Rubber Trees 

53 Industrial 

62Highways 

71 Rivers 

99 Miscellaneous 

33 Sparse Forest 

81 Mud Flats 

32 Brush/Shrubs 

72 Ponds and Reservoirs 

31 Forest 

Applications of Remote Sensing to Surveying and Mapping 11 
Figure 5. Comparison of the raw image of the Black Hills classification results and the results of smoothing or area 

filtering techniques. 

12 Applications of Remote Sensing to Surveying and Mapping 
Figure 6. The NBSM and EDC team comparing the 

ground truth data with aerial photographs. 

Anticipated final products from the 1989 4-week working 

session at the EDC were: 

1. TM, 6-band classification 

2. TM, bands 3, 4, and 5 classification 

3. TM, bands 2, 3, and 4 classification 

4. TM, tasseled cap transformation 

5. TM, principal components transformation 

6. SPOT, three-band classification 

7. TM, ground truth classes and spectral statistics 

8. SPOT, ground truth classes and spectral statistics 

9. Ground truth and topographic data contingency tables 

10. Postclassification refinement of the best results using 

digital slope data 

Research topics addressed by the above products and other 

planned work included: 

1. TM band combinations for classification 

2. TM spectral transformations for information 

3. TM vs. SPOT comparison 

4. Determination of optimum classification performance 

5. Land cover and topography relationship 

6. Textural analysis 

7. Temporal analysis (discussion only, analysis not possible with present data sets) 

Figure 7. A shaded relief image of the Ningxiang test site created with merged 1:10,000- and 1 :50,000-scale digital 

terrain model data. 

Applications of Remote Sensing to Surveying and Mapping 13 
A detailed examination of the digital ground truth 

data revealed a significant number of attribute errors. These 

errors prohibited the use of the original ground truth data 

for accuracy assessment. Correction of the errors by an 

EDC digitizing technician was not completed until three 

days before the NBSM science team's scheduled departure. 

The use of the corrected ground truth data in classification 

performance assessment is discussed later in this report. 

Figure 6 shows some of the participating scientists evaluating the ground truth data. 

During this time, an NBSM scientist evaluated the 

digital terrain data for the Ningxiang quadrangle and 

mosaicked it with the Mei Tan Ba quadrangle to the west. 

The western data set had been digitized during the third 

year of the Protocol by the NBSM using 1: 1 0,000-scale 

topographic maps. During the fourth year, the NBSM GIS 

group digitized the Ningxiang data from a 1 :50,000-scale 

map. As was expected, the data collected from the smaller 

scale topographic maps were much more sparse and 

resulted in a more coarse representation of the terrain. The 

mosaic of the two maps was very close spatially, but the 

join line between the two different resolution data sets was 

~ Intensive Test Site 

0 1:50,000 ·Scale Quadrangle 

Mel Tan Ba Nlngxlang 

Figure 8. Map showing the location of the intensive test 

site located in the TM and SPOT overlap region of the 

Ningxiang study area. 

quite apparent. An NBSM scientist met with an EDC senior 

image processing scientist in the Digital Image Sciences 

Section to determine an appropriate solution to the problem. 

A spatial filtering procedure that involved the application of 

a 1- by 5-pixel window with relative weights on either side 

14 Applications of Remote Sensing to Surveying and Mapping 

8 

Figure 9. Ellipse plots of the 36 spectral cluster classes 

calculated for TM bands 3, 4, and 5. Ellipses represent 

one standard deviation about each class mean. 

of the join line that would digitally provide a smoother transition between the two maps was developed by EDC. The 

visual result was very satisfactory considering the quality of 

the lower resolution digital terrain model (DTM) data. 

Using the resulting data set, an NBSM scientist extracted 

the test site area and generated aspect, slope, and shaded 

relief products from the DTM data values. Figure 7 shows 

the shaded relief image of the Ningxiang test site created 

with the merged DTM data sets. 

Statistics Development 

For the land cover classification task, a small 582- by 

840-pixel test site was selected and was located so that both 

the TM and SPOT data were available over the entire site 

(see fig. 8). An NBSM scientist selected thirty-six 31- by 

31-pixel cluster blocks representing the 18 land cover 

classes. These were assembled into a single composite 

image 186 by 186 pixels. Three sets of statistics were 

calculated for this 36-block image using (1) TM bands 1, 2, 

3, 4, 5, and 7; (2) TM bands 2, 3, and 4; and (3) TM bands 

3, 4, and 5. 

Ellipse plots of the class statistics from the TM 

ISOCLASS results (bands 3, 4, and 5) were evaluated (see 

fig. 9). Variations in ISOCLASS parameters controlling 

TM cluster-based intraclass standard deviations were investigated as they related to spectral signatures found in agricultural regions of China. With TM data, the maximum 

allowable standard deviation per class needed to be set at 

6.5 to have at least 80 percent of the clusters meet the 
Figure 10. An NBSM scientist checking the initial classification results using the VAX 11/780 computer at the 

ED C. 

criteria. At this point the iterative sequence of splitting 

clusters was halted and an iterative sequence of combining 

clusters was initiated. The result of this processing was a 

set of 29 spectral clusters after 9 iterations. These results, 

from an application of LAS software, were replicated on the 

Interactive Digital Image Manipulation System (IDIMS) 

using similar class means to evaluate a new LAS 

ISOCLASS implementation. 

Spectral separability between the 29 LAS-derived 

TM cluster classes was calculated using transformed divergence (LAS function DIVERGE) and resulted in an 

average divergence value of 96.5842 (which is equal to a 

~- 8 8® 

8 ®® GG §- 24 @9 880 2

7, 28 

® C") 8@)e e @) ~ ~- 0(08 < ..... m 

5;: ~- 088 

~- 88 

8 

8 

0 I I 

75100 

I 

12~ 00 I 0.00 25.00 50.00 100.00 150.00 

BAND2 

Figure 11. Ellipse plots of the 28 spectral cluster classes 

calculated using SPOT bands 1, 2, and 3. Ellipses represent one standard deviation about each class mean. 

divergence value of 1,932 on the traditional 2,000-level 

saturation scale). IDIMS results gave a transformed divergence value of 1,941. Those more familiar with MSS data 

might note that this is a much higher average divergence 

than expected with the wider band MSS data. The use of 

8 

~-

~~----~----~----.-----.-----.-----. 

o.oo 2s~oo so~oo 751

oo 1~ 00 12~ 00 1s6.oo 

BAND3 

Figure 12. Ellipse plot for the best TM two-band combination (bands 3 and 4) calculated from spectral values in 

TM bands 2, 3, and 4. 

8 

~-

8 

~-

~- t2v @ee@ G 

< 

Ill 

~ ..... ~- ~@@(§) @®® m @ e @ 

5;: ~- @G) ~ B 

0 ~- 08 tiJ 

0 

8 

(j) 

0 

25~00 I 

75100 

I 

12~.00 I 0.00 50.00 100.00 150.00 

BAND4 

Figure 13. Ellipse plot for the best TM two-band combination (bands 4 and 5) calculated from spectral values in 

TM bands 1, 2, 3, 4, 5, and 7. 

Applications of Remote Sensing to Surveying and Mapping 15 
TM band 5 (middle-infrared) seemed to contribute to 

greater TM class separability. 

An NBSM scientist worked with the cluster results, 

aerial photographs, and ground truth map to link the 29 

cluster classes with the corrected ground truth information 

classes (fig. 1 0) . He found that a relliionable relationship 

could be made between these for 10 information classes. 

The SPOT data (multispectral bands 1, 2, and 3) were 

then classified using statistics developed from the same 

ground-referenced training locations as had been used for the 

TM data. The training locations were thirty-six 31- by 31-

pixel blocks (7.14 percent of the test site pixels) that were 

mosaicked into a single image and then clustered using 9 

iterations with (1) maximum number of clusters specified at 

36, (2) minimum cluster class size at 30 pixels, (3) maximum 

within-cluster standard deviation set to 6.5, and (4) minimum 

intraclass separability at 2.3. The resulting SPOT 

ISOCLASS statistics contained 28 cluster classes. An ellipse 

plot of the cluster results is shown in figure11. 

The classification test site was then expanded to the 

larger TM data set (1,463- by 2,440-pixels). Because there 

was a significant change in land use and land cover types in 

the eastern portions of the Ningxiang map sheet, which was 

not apparent in the smaller central test site used earlier, it 

was necessary to select additional training sites for these 

cover types. Consequently, 12 additional 31- by 31-pixel 

samples were chosen to represent the change in agricultural 

practices, the more rugged terrain, and the more heavily 

forested areas. These 12 sites were added to the 36 previous sites, and the ISOCLASS algorithm was run on the 

new mosaicked 48-block, 248- by 186-pixel training data 

set. The resulting 36 clusters were obtained using the 

identical cluster parameter settings as used with the 

smaller TM test site. 

Having become familiar with the TM data through the 

selection of training sites and the refinement of the spectral 

statistics for the band 3, 4, and 5 subset, the scientists began 

processing the other classification products. Specifically, 

class statistics were calculated for the various band combinations and refinements were made prior to generating classification results for TM band combination 2, 3, and 4 and 

TM band combination 1, 2, 3, 4, 5, and 7. Ellipse plots, 

divergence values, and ISOCLASS cluster maps were used 

to refine and identify the 19 cluster classes for the band 2, 3, 

and 4 combination and the 36 cluster classes for the band 1, 

2, 3, 4, 5, and 7 combination. Ellipse plots for the best 

combination of two bands from each of these two statistical 

files are shown in figures 12 and 13. 

Classification Approach 

The BAYES maximum likelihood algorithm used 29 

cluster class statistics to classify the smaller TM study site 

and the 36 class statistics to classify the larger TM site. A 

16 Applications of Remote Sensing to Surveying and Mapping 

chi-square image also was calculated for each classification 

result to measure the probability of correct classification for 

each pixel in the study site. A color Calcomp plot of the 10 

information classes resulting from the classification was 

produced at a scale of 1:30,000. 

Postclassification Processing 

Because of the complex land cover patterns in this 

area of China and the extremely small field sizes, the scientists decided that nominal filtering techniques (Fosnight, 

1987) might enhance both the visual rendering of the classification and the performance statistics for the final classification product. Consequently, after consulting with an 

EDC scientist concerning the theory and implementation of 

nominal filtering techniques, the scientists ran two tests on 

the TM bands 3, 4, and 5 classification results. Nominal 

filtering allows the analyst to spatially filter the classification result while controlling the size of the regions, the 

classes affected, and the classes to which the regions are 

reassigned. This process requires a matrix of class-pair 

weights to specify which class reassignments are allowable. 

The matrix was developed for the test site on the basis of 

spectral statistics for each class. If a very small region is 

similar to its neighboring regions, then it is acceptable to 

reassign that small region. In addition, the class of the 

larger neighboring region that is adjacent to or surrounds 

the small region is the class to which the small region is 

reassigned, if the spectral statistics are acceptably similar on 

the basis of the weighing table. 

The nominal filtering tests that were conducted used 

a weight table that was calculated from the ISOCLASS 

statistical file. The difference between the two tests was 

only in the size of the regions that were candidates for 

class reassignment. The first of these two conservative 

tests were for regions with only an isolated pixel whose 

class was different from surrounding pixels. The second 

test used regions two pixels or less in size. The singlepixel test filtered 3.94 percent of the pixels from the TM 

band 3, 4, and 5 classification result and had little visual 

impact. The two-pixel test filtered 14.91 percent of the 

pixels in the test site and gave a much more visually 

appealing classification map. Classification performance 

is discussed later in this report. 

Accuracy Estimation 

An NBSM scientist suspected that the registration of 

the ground truth data to the satellite data that was performed 

in Beijing might be off by as much as two pixels. 

Consequently, to allow for this two-pixel margin, the polygon 

outlines of the vector ground truth data were converted to 

raster format and transferred to LAS as a raster image file to 

mask out questionable areas during accuracy tests. A buffer 

zone two pixels wide on either side of each polygon boundary 
Figure 14. An NBSM scientist comparing land cover 

categories between the cluster map and the ground 

truth map. 

was created using the LAS SPREAD function. These potential misregistration buffer zones were then used to mask out 

all ground truth data in those regions so that only the central 

core of each ground truth polygon would be used when calculating accuracy. This process prevented the potential twopixel misregistration from affecting the performance results. 

However, it also might have given an artificially high evaluation of performance because many of the actual edge, or transition, pixels were also excluded from contributing to the 

calculation of accuracy. 

Because of the difficulty in uniquely identifying each 

cluster class with one of the 19 land use classes, both 

cluster and ground truth data were grouped into five land 

cover categories for comparison during the accuracy 

assessment portion of the project. These categories were 

water, forest, rice, urban, and other agriculture. Figure 14 

shows an NBSM scientist comparing cluster map and land 

cover map categories. 

Table 2 lists the ground truth land cover classes and 

the categories they were assigned. Table 3 details each of 

the TM and SPOT cluster classes and the land cover information class to which each cluster class was assigned. 

Tables 4-8 are contingency tables for the six classification 

vs. ground truth comparisons. NBSM staff evaluated these 

tables after their March 1989 working session at the EDC. 

Accuracy Assessment 

Tests showed that the overall accuracy of the classifications was slightly better than 50 percent. The cause of the 

rather low accuracy was probably because, in China, individual fields are small but grow a large variety of crops. 

This cropping pattern results in many individual pixels that 

represent a mixture of crops, making it difficult to generate 

cluster classes to identify a single crop. 

Table 2. Ground truth land cover classes compared 

with the final accuracy categories used for each of the 

classification results. 

Original data Accuracy 

Pixel value & name Category value & name 

11 Irrigated Rice 3 Rice 

12 Dryland Agriculture 5 Other Agric 

13 Irrigated Agriculture 5 Other Agric 

21 Fruit Trees 5 Other Agric 

22 Mulberry Trees 5 Other Agric 

23 Tea 5 Other Agric 

24 Rubber Trees 5 Other Agric 

31 Forest 2 Forest 

32 Brush/Shrubs 5 Other Agric 

33 Sparse Forest 5 Other Agric 

41 Natural Grasslands 5 Other Agric 

51 Cities & Towns 4 Urban 

52 Rural Residential 4 Urban 

53 Industrial 4 Urban 

62 Highways 5 Other Agric 

71 Rivers 1 Water 

72 Ponds & Reservoirs 1 Water 

81 Mud Flats 5 Other Agric 

99 Miscellaneous 5 Other Agric 

Applications of Remote Sensing to Surveying and Mapping 17 
Table 3. The six original classifications grouped into ten land cover information classes. 

Original Land Cover Information Classes 

Classification 1 2 3 4 5 6 7 8 9 10 

TM bands 2,3,4 1 2 3 5 16 17 18 4 6 10 11 19 8 9 7 12 14 13 15 

small site 

(19 classes) 

TM bands 3,4,5 1 2 3 6 7 8 20 2127 5 9 10 26 28 29 22 12 13 15 23 25 16 17 18 19 

small site 4 11 14 24 

(29 classes) 

TM bands 1,2,3,4, 1 2 3 10 1112 31 32 5 613 25 34 35 16 33 15 17 21 18 24 26 22 27 29 

5, 7, small site 4 7 8 30 14 19 20 36 23 28 

(36 classes) 9 

TM principal 30 33 35 25 28 29 3 5 6 18 19 20 1 2 4 15 23 16 26 27 12 17 24 1114 13 

components 36 7 22 21 31 32 34 8 9 10 

(36 classes) 

TM large area 1 2 3 7 8 9 22 23 28 6 1112 25 26 27 24 10 13 21 14 15 17 19 20 35 33 34 

study site 4 5 16 29 31 18 30 32 36 

(36 classes) 

SPOT bands 1 ,2,3 1 2 3 613 14 15 16 4 5 7 18 19 20 10 17 1121 9 12 23 25 26 27 28 

small study site 8 22 24 

(28 classes) 

Table 4. Contingency table for TM bands 2, 3, and 4 classification results. 

Ground Truth Categories 

Test Category Water Forest Rice Urban Other agric Total 

Water 4,026 2,716 3,640 758 3,944 15,084 

Forest 2,278 38,743 23,248 512 36,277 101,058 

Rice 6,360 22,827 103,284 1,358 55,248 189,077 

Urban 739 958 374 3,101 4,509 9,681 

Other agric 3,695 23,212 28,813 1,358 83,581 140,659 

Masked pixels 1,252 4,361 10,303 452 12,753 29,121 

Total 18,350 92,817 169,662 7,539 196,312 484,680 

228.709 correctly classified 

455,559 total test pixels = 50.204% correct 

18 Applications of Remote Sensing to Surveying and Mapping 
Table 5. Contingency table for TM bands 3, 4, and 5 classification results with five ground truth categories. 

Ground Truth Categories 

Test Category Water Forest Rice Urban Other agric Total 

Water 4,294 3,213 4,009 961 2,607 15,084 

Forest 2,548 44,502 25,335 1,198 27,475 101,058 

Rice 7,262 29,041 116,385 2,787 33,602 189,077 

Urban 608 955 427 4,167 3,524 9,681 

Other agric 4,117 31,314 31,789 3,133 70,306 140,659 

Masked pixels 1,341 5,459 11,774 695 9,852 29,121 

Total 20,170 114,484 189,719 12,941 147,366 484,680 

239.654 correctly classified 

455,559 total test pixels = 52.606% correct 

Table 6. Contingency table for TM bands 3, 4, and 5 classification results, which were refined using nominal filtering 

(two-pixel region refinement), with five ground truth categories. 

Ground Truth Categories 

Test Category Water Forest Rice Urban Other agric Total 

Water 4,293 3,071 4,223 921 2,576 15,084 

Forest 2,426 45,277 25,656 1,019 26,680 101,058 

Rice 7,081 27,771 119,593 2,600 32,032 189,077 

Urban 590 900 423 4,268 3,500 9,681 

Other agric 4,188 30,987 32,717 2,775 69,992 140,659 

Masked pixels 1,368 5,321 12,060 675 9,697 29,121 

Total 19,946 113,327 194,672 12,258 144,477 484,680 

243.423 correctly classified 

455,559 total test pixels = 53.434% correct 

Applications of Remote Sensing to Surveying and Mapping 19 
Table 7. Contingency table for TM bands 1, 2, 3, 4, 5, and 7 classification results with five ground truth categories. 

Ground Truth Categories 

Test Category Water Forest Rice Urban Other agric Total 

Water 4,571 3,236 3,154 806 3,317 15,084 

Forest 3,549 51,318 15,021 384 30,786 101,058 

Rice 9,979 38,830 101,280 1,235 37,753 189,077 

Urban 826 472 277 2,929 5,177 9,681 

Other agric 5,867 34,647 21,641 1,054 77,450 140,659 

Masked pixels 1,715 6,614 9,571 323 10,898 29,121 

Total 26,507 135,117 150,944 6,731 165,381 484,680 

237.548 correctly classified 

455,559 total test pixels = 52.144% correct 

Table 8. Contingency table for TM principal components classification using the first two components of the six calculated 

with five ground truth categories. 

Ground Truth Categories 

Test Category Water Forest Rice Urban Other agric Total 

Water 4,349 1,654 4,314 1,103 3,664 15,084 

Forest 3,307 27,821 30,844 773 38,313 101,058 

Rice 8,583 16,381 120,784 2,670 40,659 189,077 

Urban 505 140 504 4,798 3,734 9,681 

Other agric 5,032 13,133 38,772 2,853 80,869 140,659 

Masked pixels 1,570 2,937 12,491 860 11,263 29,121 

Total 23,346 62,066 207,709 13,057 178,502 484,680 

238.621 correctly classified 

455,559 total test pixels = 52.380% correct 

20 Applications of Remote Sensing to Surveying and Mapping 
Difficulty in spectral classification also arises because 

different crops may have the same band-for-band spectral 

signature. It is also possible that the same crop has different 

spectral signatures because of differences in planting dates, 

water availability, and so on. Because classification 

assumes unique spectral signatures for the desired classes, 

the above conditions limit the accuracy achieved. 

An examination of the results of the various TM 

classifications showed that the 2, 3, and 4 band combination had a lower accuracy than the 3, 4, and 5 band combination. This result was because bands 2 and 3 tend to be 

highly correlated and do not provide as much information 

for classification. The combination of bands 1, 2, 3, 4, 5 

and 7 also resulted in a lower accuracy than the 3, 4, and 5 

band combination. This result was probably because of 

the high correlation of bands 2 and 3, as previously 

mentioned, and of bands 5 and 7. The combination of 

bands 3, 4, and 5 appears to be the best for classification. 

Normal filtering techniques also resulted in some improvement in the classification accuracy. This procedure allows 

the analyst to control the merging of individual or small 

clusters of pixels into the appropriate cluster class. 

The final step in the classification investigation was 

to calculate the similarity between each of the classification results using the S statistical package on a Sun workstation. This process was done using the five land cover 

cate~ories to generate contingency tables an"d to calculate 

the K statistic (Cohen, 1960). The detailed K statistics are 

shown in table 9. In summary, each of the six classification results are different from one another. 

Table 9. Correlation of K statistics for each classification 

result, to ground truth calculated using the S statistical 

package. [Note that each classification result is significantly different from the others]. 

1\ 

Classification K v vo 

SPOT 123 0.3733417 4.71506E-6 4.062468E-6 

TM345Nomfil 0.3354755 5.29683E-6 4.376745E-6 

TM 123457 0.3300715 5.055506E-6 4.19341E-6 

TM345 0.3254116 5.27819E-6 4.34815E-6 

TMPC1&2 0.3080216 5.325085E-6 4.385llE-6 

TM234 0.2919653 5.352671E-6 4.40996E-6 

The magnitude of the V and VO values indicate, respectively, that each of the classification results is significantly 

different from the others (V) and that these differences 

can not be attributed to random occurrences (VO). That is, 

using the same ISOCLASS parameters to calculate the 

spectral classes with the six different band sets, six significantly different classifications were derived. 

Other Processing 

OEM Derivatives 

During the EDC team's visit to the NBSM in the 

third year of the Protocol there was significant interest in 

modeling the physiographic characteristics of the study 

area using DTM data and some of the techniques developed as part of the drainage research conducted by an 

EDC scientist. An NBSM scientist met with an EDC 

scientist during the visit to the EDC and discussed these 

techniques and how they might be used to identify local 

topographic flat areas in the study area that are upland 

highs rather than valley lowlands. The scientist processed 

the DTM data for a 545- by 840-pixel test area and generated a flow direction image wherein individual pixel 

values indicated the number of other pixels that "drained" 

to it and a level image that identified flat areas that had 

local "pits" removed from them. These images were 

recorded on tape for an NBSM scientist to study after 

returning to Beijing. It was expected that the flat uplands 

would be easily discernable because of their relatively low 

values. Consequently, these data then could be used to 

refine classification results where spectral classes similar 

to rice were misidentified in the drier uplands. 

Because rice and dryland crops are combined in the 

same spectral class, DTM data were tested to determine if 

topographic position could be used to separate these spectrally similar crops. The DTM data did not clearly distinguish the slope differences between the flat rice cropland 

and the flatter areas of the hilltops. Perhaps a combination of slope determinations as well as elevation differences would provide criteria for their separation. 

Data Transformations 

Principal Components Analysis 

In addition to the classification studies, an NBSM 

scientist used the personal computer assigned to the project 

to generate principal component images from the six bands 

of TM data. He determined that the first two components 

that were calculated contained the most spectral information. This effort was done to build a smaller hybrid data set 

for processing on the smaller NBSM PRIME minicomputer 

to preserve as much of the spectral information as possible 

and, subsequently, to assess its performance as a proxy data 

subset for classification purposes. Figure 15 shows an 

image of the first principal component. 

Texture Analysis 

The NBSM team at the EDC also investigated use of 

image texture measures with the LAS TEXTURE function 

to calculate energy (second moment) and variance. Initial 

Applications of Remote Sensing to Surveying and Mapping 21 
Figure 15. Image of principal component one resulting from the processing of the six bands of TM data. 

results indicated that the energy measurement maintains 

image structure while emphasizing the low texture rice 

areas. Variance appeared to be similar to gradient or edge 

enhancement results and, therefore, would not be a useful 

measure of texture for post-classification refinement in 

terms of information class differentiation. 

Beijing TM/SPOT Image Merge 

To experiment with multisensor image merging techniques, the NBSM provided both SPOT panchromatic data 

and TM data over Beijing. The SPOT scene was registered 

to a map base and served as the reference for the TM 

image-to-image registration. The two scenes were merged 

using HIS techniques. The resulting scenes were enhanced 

and then printed as 1:50,000- and 1: 1 00,000-scale 

photographs. Figure 16 shows the resulting image. 

CONCLUSIONS 

Evaluation of Products 

Through this cooperative work, image maps and land 

22 Applications of Remote Sensing to Surveying and Mapping 

use maps were produced from satellite data . Two 

1 :50,000-scale maps of the Ningxiang test site, overlain with 

10,000-meter grid lines and principal geographic placenames, were completed. Satellite image mosaics of the 

Black Hills study area in the US were produced at scales of 

1: 100,000 and 1:250,000. A 1: 1 00,000-scale image map of 

the Rapid City quadrangle was also produced using TM data. 

The Rapid City image map was a new product that 

combined satellite imagery and linework on the same map. 

The traditional line map was printed on the reverse side of 

the image map to provide the basic map information. The 

satellite image background of the image map provides visual 

information about the land surface that is extremely helpful 

in the interpretation of the map. Even though the TM image 

is of lower resolution than the aerial photographs, it can meet 

the requirements of 1: 1 00,000-scale maps of China. The 

added image processing that was used to produce the image 

map improved the overall quality of the images used. 

Land use maps of the Ningxiang test site were 

produced showing that Level I land use categories could be 

defmed. In some cases Level II categories were also defined. 

These maps can meet the demand for 1: 1 00,000-scale land 
Figure 16. Image of Beijing resulting from the TM/SPOT merge and enhancement. 

use maps of China. Because China is a country of vast territories and a large population, the importance of low-cost and 

rapid production of maps is vital to their resource investigations and land planning processes. Of added importance is 

the ability to frequently produce these maps to determine 

changes in land use. 

Benefits of the Technical Exchange 

Through the technical exchange described in this 

report, many of the key problems and technical processes 

involved in the production of satellite image maps and land 

use maps were resolved. Scientists from the NBSM were 

exposed to many advanced techniques in image processing 

and were able to put many of these techniques into practice. 

The investigations gave the USGS scientists a better understanding of how to apply image processing techniques to the 

rather unique geography of China. The small fields and 

complex cropping patterns of the land cover pointed out the 

need for further improvements in image processing techniques. 

These cooperative experiments also led to decisions 

about the best approaches to land use mapping by showing 

the effects of different processing methods on classification 

results, the optimum TM band combinations to use, and the 

benefit of using ancillary data for improving classification 

accuracy. Perhaps most importantly, this cooperation 

strengthened the friendship and understanding of the mutual 

problems faced by scientists of both countries. 

Technical publications (Lauer and Chu, 1988, and Qiu 

and Len, 1990) were generated as a result of the exchange. 

These publications further enhanced the relationship 

between the two countries. 

Applications of Remote Sensing to Surveying and Mapping 23 
SUMMARY AND RECOMMENDATIONS FOR FUTURE 

WORK 

This cooperative effort was successful in applying 

remote sensing techniques to surveying and mapping and in 

promoting further development of such techniques. The US 

and the PRC agreed that the scientific and technical cooperation should continue for an additional 5 years with the 

following objectives: 

1. Evaluate, and adopt where appropriate, standards for 

data processing, common data processing flows, and 

new data analysis techniques. 

2. Investigate methods for exchanging, processing, and 

analyzing Landsat data, 1-km resolution Metsat data, 

and data from other satellite systems that can be integrated with other types of spatial data. 

3. Conduct joint research on software development for 

thematic mapping, GIS integration, and change detection. 

We look forward to continuing the research that was so 

successfully conducted in the previous term of the Protocol. 

REFERENCES 

Lauer, D.T., and Chu, L., 1988, Sino-american cooperative 

studies on applications of remote sensing to surveying 

and mapping, in International Symposium on Remote 

Sensing of Environment, 21st, Ann Arbor, Michigan, 

October 26-30, 1987, Proceedings: Ann Arbor, 

Environmental Research Institute of Michigan, p. 

591-595. 

Qiu, Z., and Lin, F., 1990, Investigation of application 

potentiality and accuracy assessment of TM and 

SPOT images for land use mapping: Geodaetica et 

Cartographica Beijing, p. 1-5. 

Fosnight, E.A., 1987, Applications of spatial postclassification models, in International Symposium on Remote 

Sensing of Environment, 21st, Ann Arbor, 

Michigan, October 26-30, 1987, Proceedings: Ann 

Arbor Environmental Research Institute of 

Michigan, p. 469-485. 

Cohen, J., 1960, A coefficient of agreement of nominal 

scales: Educational and Psychological Measurements, 

v. 20, no. 1, p. 37-46. 

24 Applications of Remote Sensing to Surveying and Mapping