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QUARTERLY PROGRESS REPORT

FOR

LANDSAT-4 IMAGE DATA QUALITY ANALYSIS

FOR PERIOD INCLUDING

NOVEMBER 10, 1983 - FEBRUARY 9, 1984

NASA CONTRACT NAS5-26859

(E84-10134) LAhOSAT-4 IMAGE CATA QUALITY N84-26082

ANALYSIS Quarterly Progress Report, 10 Nov.

1983 - 9 Feb. 1984 ( Purdue Univ.) 4 p

HC A02/MF A01 CSCL 05B Unclas

G3/43 00134

T0: NATIONAL AERONAUTICS & SPACE ADMINISTRATION

GODDARD SPACE FLIGHT CENTER

GREENBELT POAD

GREENBELT, MD 20771

BY: P.E. ANUTA AND STAFF

PURDUE UNIVERSITY

LABORATORY FOR APPLICATIONS OF REMOTE SENSING

WEST LAFAYETTE, It! 47906-1399
Introduction

This report covers research carried out on Landsat-4

data under NASA Contract NAS5-26859 for the period November

10, 1983 through February 9, 1984. The primary activity in

this period was completion of analysis items needed for a

comprehensive paper discussing the results of the study to

date. Work on a layered classifier experiment continued to

complete a comparison of Thematic Mapper classification

results.

Problems

No data on contractual problems occurred during the

period.

Publications

A paper was prepared and submitted to IEEE Transactions

on Geoscience and Remote Sensing, entitled "Landsat-4 MSS

and Thematic Mapper Data Quality and Information Content

Analysis." It is to be published in the May 1984 issue.
Recommendations

No recommendations are made in this report.

Funds Expended

The funds expended in the project are reported periodically by the Purdue University Office of Contract and Grant

Business Affairs to the sponsor on NASA Form 533M. These

are issued monthly. Specific disclosure of funds expended

in this report is not a policy of the University.

Significant Results

In the previous quarter, comparative evaluation was

made of classification performance using point classifiers

for TM and MSS data. Additional work has been performed

using the contextual classification program 3ECHO and principal components point classification on TM data. The

results for the two new cases are listed in the table below

along with the results for the three cases listed in the

previous report.
Classification Accuracy and CPU Time Comparison on Test Data in the

Des Moines Area.

Scene iD: 40049-16264

TM TM TM MSS

GML Per-Point GML Per-Point SECHO GML Per-Point

Classifier Classifier Classifier Classifier

(All 7 Bands) ( Best 4 Bands) ( All 7 Hands) (All 4 Bands)

CLASS %C^,rrect %Correct $Correct %Correct

Forest 99 .0 97.1 100.0 91.2

Corn 92.0 76.8 97.7 30.8

Soybeans 100.0 99.8 100.0 99.3

Bare Soil 99.7 99.0 100.0 55.6

Grass 96.8 87.6 98.1 1.9

Water 100 . 0 96.8 100.0 98.9

Urban 91.7 ?9.9 95.8 50.2

OVERALL 95.7 92.6 97.9 67.4

Note that the SECHO results are better than any of the other cases.

This indicates that the use of spatial in conjunction with spectral

data is significant even though there is some blurring of spatial

` structure with the SECHO algorithm. The principal component result

is unexpected as previous experience has shown that the PC result

should be better than 'che result for the same number of features of

the original data,. The reason for this is not known and this result

is being investigated further.

3