Journal of Global Resources
Volume 6 (01)August 2019-January 2020
Page 08-15
UGC–CARE Listed Journal in Group D
ISSN: 2395-3160 (Print), 2455-2445 (Online)

02
INCORPORATING SPECTRAL INDICES AND TEXTURAL FEATURES FOR IMPROVED
CLASSIFICATION ACCURACY TOWARDS SEMI-AUTOMATED RIVER SAND MAPPINGUSING HIGH-RESOLUTION MULTISPECTRAL SATELLITE IMAGERY
Virat Arora1,2, S. S. Rao1, E. Amminedu2 and P. Jagadeeswara Rao2
1
National Remote Sensing Centre, Hyderabad, India
2
Dept. of Geo-Engineering, Andhra University College of Engineering, Visakhapatnam, India
Email: aroravirat@gmail.com
Abstract: For the ephemeral river channels in semi-arid regions of India, after every Monsoon
season, prompt preparation of river sand distribution maps is often necessary for river sandauditing before resuming the sand mining operations. The process can be readily assisted byclassifying the satellite-based remotely sensed imagery, although often confronted by limited
accuracy levels arising due to poor distinguishing capability among the spectrally similar classcategories. This study aims to improve the classification accuracy targeting river sand deposits
by systematically examining the effects of including spectral indices and textural features in thefeature-space while classification. Two most common classification algorithms, viz. Maximum
Likelihood Classification (MLC) and Support Vector Machine (SVM) classification were used.
The results show that SVM performed even better when Normalized Difference Vegetation
Index (NDVI) and correlation texture feature computed at 3x3 window size were included in thefeature-space comprising original spectral bands.
Key words: River Sand, MLC, SVM, Spectral Indices, Textural Features
Introduction
River sand is a valuable natural resource with its major utilization in the construction industry.
With the increasing pressure of rapid infrastructural development, there has been an equivalentrise in the market demand of river sand. Consequently, the limited sources of river sand areplaced with an undue strain in terms of disturbed riverine environment. To ensure thesustainable use of river sand, regular audits of their reserves are often recommended by thepolicy makers. Preparation of river sand distribution maps is an important step in river sandaudits. Regular updating of such maps requires frequent cumbersome field surveying spells
that usually encompasses involvement of multiple stakeholders. In this scenario, often, old data
are relied upon and are carried forward year-by-year without updating due to lack of availabilityof resources, and more importantly, due to lack of knowledge of appropriate technique (Mitra
and Singh, 2015). Remote sensing data has demonstrated potential in the applications
involving generation of planimetric maps depicting river sand deposits. For example,
Ramkumar et al. (2015) made an attempt to identify the active-channel sand bars within Kaveri
River through visual interpretation from IRS 1B LISS-III imagery of the year 2008, compared itwith Survey of India toposheet of the year 1971, and found the areal extent of these sand bars
to be increasing at a rate of 1.05 km2 per year (Ramkumar et al., 2015).
For eliminating the problem of biasedness in image interpretation, and towards
introducing automation, numerous classification algorithms have been developed by the
research community such as pixel-based, object-based and knowledge-based classification
algorithms that use spectral, spatial or temporal information, or any combination of these, via
unsupervised clustering methods or supervised learning methods (Lu and Weng, 2007). For
example, Leckie et al. (2005) performed pixel-based image classification on 0.8 m spatial
resolution aerial imagery dataset with eight spectral bands for mapping of stream features suchas deep, moderate and shallow classes of water along with sand, gravel, cobble, and rocky
areas. Feature-space optimization is a critical step towards increasing the classification
accuracy. Features such as spectral signatures, vegetation indices, textural and terrain featuresare some of the possible variables in any classification process (Lu and Weng, 2007). The key
is to improve the class-separability by incorporating the distinguishing characteristics among
the land cover classes present in the study area. Various spectral indices, when included in the8

feature-space, have contributed towards improving the overall accuracy of classification (Zha etal., 2003). Normalized Difference Vegetation Index (NDVI), developed by Rouse et al. (1973) is
one of the commonly used indices. Also, Normalized Difference Water Index (NDWI) is
specifically used to enhance the surface water features and works as a complementary index
along with NDVI (Zha et al., 2003). By using NDWI, estimation of turbidity in water may be
performed (McFeeters, 1996). To enhance the separation of completely bare, sparse anddense vegetation cover, Bare Soil Index (BSI) is used (Azizi et al., 2008).
Further, incorporating image texture properties for improved classification accuracy has
been suggested by many studies like Haralick et al. (1973) and Huang et al. (2014). Image
texture is a measure that depicts spatial arrangement of the pixel grey levels (Carbonneau etal., 2005). Although there are several measures available to harness textural properties fromimage data (Chaurasia and Garg, 2013), but Grey Level Co-occurrence Matrix (GLCM) is oneof the widely utilized methods in case of satellite imagery (Haralick et al., 1973; Huang et al.,2014; Li et al., 2011). However, it is important to consider about the choice of texture feature
and window size to adopt for the study (Pathak and Dikshit, 2010). The present study attempts
to make an assessment on the hypothesis that for the spatial mapping of river sand, spectral
indices and textural features improve the accuracy of supervised classification on themultispectral satellite imagery. Various combinations of features were prepared and each of the
combinations was examined using two most prevailing supervised classification methods,
namely, Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM). MLC isa parametric classification method wherein it is assumed that the land cover features arenormally distributed and hence, to derive the statistics, a sufficiently large number of training
samples are required for MLC (Richards and Jia, 1999). Whereas, SVM, which has sharply
gained popularity in remote sensing (Mountrakis et al., 2011), is a non-parametric machinelearning algorithm where parameters such as mean vector and covariance matrix are not used,and hence, any kind of assumption about the data is not required (Lu and Weng, 2007). The
reader is referred to Lu and Weng (2007) for a detailed description of various remote sensing
image classification methods.
Study Area
A rectangular extent (bottom-left coordinates: 72°32’13.484’’E 25°46’57.250’’N; top-right
coordinates: 72°36’29.242’’E 25°49’23.828’’N) of area 3157.5 ha comprising part of Luni Rivernear Samdari town of Barmer District in Rajasthan was selected as the study area (Figure 1).
The area was identified keeping in view the presence of maximum possible number of land
cover classes representing the semi-arid climatic region so as to test the distinguishing ability of
the image-processing classifiers and the feature-spaces being used in the study.
Figure 1: False Colour Composite of Planet Labs Data (3 M Spatial Resolution)

9

Dataset Used
A four-band (blue, green, red and near-infrared wavelength regions) multi-spectral imagery(date of pass: 08 September 2017) acquired by the Planet Scope constellation of dovesatellites (Planet Team, 2017) was utilized for the present study (Figure 1). The access to theimagery-dataset was granted for academic research under Planet’s Education and Research
program. The data, in WGS 1984 spatial reference system and UTM Zone-43 projection, wassupplied with geometric corrections & orthorectification pre-applied. Cubic Convolution
resampling method was adopted while projecting the imagery. The resampled pixel size for theimagery was 3 m. Pixel digital number (DN), in 16-bit radiometric resolution, was surface
reflectance value incorporated with the scaling factor of 10000. Field visit in the study area wasconducted during the month of September 2017 (Figure 2) although limitations were enforcedby the local police due the widespread threat of sand mafia subsequent to the ban imposed by
the Hon'ble High Court of Rajasthan. Finer spatial resolution imagery of a similar date sourced
from Google Earth was primarily referred during the research work.
Figure 2: (a) Vegetation present on inactive portions of river sand deposits; (b) Class
boundary of coarse-grained and fine-grained river sand cover as pictured during thefield visit

(a)

(b)
Methodology
Varying input feature-spaces were tested for classification accuracy using two image
classification methods viz. MLC and SVM targeting riverine sand mapping. The processincluded derivation of spectral indices and textural features that were used to construct thefeature-spaces. Subsequently, classification steps were performed repeatedly and resultant
accuracies were analyzed to find the best suited method for river sand mapping. The detailed
steps are listed below (as also graphically illustrated in Figure 03): For identifying the possible
number of land cover types present, initially, a visual interpretation was performed over thefalse color composite of the study area. Additionally, spectral clustering was performed using
ISO Cluster Tool in ArcGIS 10.6.1 which was programmed to generate 100 clusters initially,that were later grouped to form 10 meaningful classes. In this manner, a classification scheme
was designed while focusing on the riverine environment (Table 01). Training sample areas foreach class were delineated with the help of field knowledge and the higher resolution imageryof a similar date as available from Google Earth. Samples were improved by testing them forpurity and separability by means of a preliminary Maximum Likelihood Classification using thespectral bands. Once finalized, the same set of sample areas was used in the subsequent partof the experiment.
Spectral indices (NDVI, NDWI and BSI) were generated from the input multi-band imagery
using Raster Calculator tool in ArcGIS 10.6.1.
(
) (
)
(
) (
)
[(
] [(
]
)
)
For incorporating textural information, second-order texture filters were applied on thegrey-level co-occurrence matrix (GLCM) using ENVI 5.4. The filters included mean, variance,
homogeneity, contrast, dissimilarity, entropy, second moment and correlation. Greyscale
10quantization level was set to 8-bits in order to reduce the processing time. Four sets of varying
window sizes (3x3, 5x5, 7x7 and 9x9) were prepared, each set comprising the above
mentioned eight filters.
Various combinations of the spectral bands, spectral indices and/or textural features
were prepared using Composite Bands Tool as available in ArcGIS 10.6.1.
Every combination was tested for the performance by MLC and SVM classifiers as available in
ArcGIS 10.6.1. For a given feature-space, first, the classifier was trained using the sample
areas. The trained classifier was then applied to the imagery of given feature-space covering
the full study area. For executing the repeated steps, routines were created using the ArcGIS
Model Builder.
For the accuracy assessment, testing sample areas were delineated avoiding thelocations of training sample areas. For this, a manual classification of the same image was
performed at the mapping scale of 1:10000 using image interpretation techniques supported by
the ground truth evidence as collected by the field visits and information from Survey of India
Topographic Maps, Bhuvan Geo-portal of National Remote Sensing Centre (Indian Space
Research Organisation) as well as the similar date higher resolution imagery as available fromthe Google Earth platform.
Sample points for testing were generated adopting the Stratified Random Samplingstrategy on the manually classified image. The sample points were randomly distributed withineach class, the number of points in each class being proportionate to the relative area of theclasses. In all, 512 points were generated by using Create Accuracy Assessment Points tool in
ArcGIS 10.6.1. The same set of test sample points was used in the accuracy assessment of all
the classified outputs generated in step 6. Confusion matrix was generated for each of theclassification output.
Figure 03: Flowchart of the Methodology followed

IndicesNDVI, NDWI,
BSI

4Spectral Bands

3m Spatial
Resolution
Multi-spectral
Imagery

1

2

Designing theClassification
SchemeIdentifying theTraining
Sample Areas

GLCM
TextureFeatures8 types offeatures withvarying
window sizes

Various
Combinations
to form the
Feature Space

Ground-Truth /Higher Resolution
Remotely Sensed
Imagery
7
Manual Classification
at the mapping scaleof 1:100003

5

Training theclassifiers

8

MLC & SVM

6

Applying theclassification
on full image
MLC & SVM

Designing Test Data
using Stratified
Random Sampling

9
Accuracy Assessment
for each of the
Combination

Table 01: Classification Scheme Adopted for the Present StudyClass
1Built-Up
2
Dense-Vegetation
3
Sparse-Vegetation
4
Bare-Soil
5
Rocky-Area
6
Non-Turbid-Water
7
Turbid-Water
8
Wet-River-Sand
9
Coarse-River-Sand
10 Fine-River-Sand

Description
Residential buildings including transportation roads
Trees, bushes and standing healthy agricultural crops
Pasture-lands / grasslands / sparse vegetation coverLand devoid of vegetation and any other inhabitation; includes fallow land
Barren rocky; includes open pit rock mining areas present in the study area
Water exhibiting darker tone
Water exhibiting lighter tone due to mixed reflectance from sand bed materialRiver sand saturated by water
Sand deposits within river channel with less bright / grey tones
Sand deposits within river channel with brighter tones

11Results and Discussion
The Kappa values derived from confusion matrices of the classification outputs weregraphically analyzed to study the effect of varying the feature-space during classification. The
results are grouped into three sets of effects, depending upon the additions to the featurespace:
Effect of Adding Spectral Indices to the Feature-SpaceIn the first set, Kappa values were assimilated for cases when only spectral indices viz. NDVI,NDWI and BSI were added to the composite of original spectral bands as shown in Figure 04.
With SVM, better results were achieved when NDVI was included with the spectral features. No
improvement was seen when NDWI was also added to this composite. Whereas, BSI adversely
affected the results whenever included in the feature-space. In the case of MLC, no
improvements were seen with the inclusion of any of the spectral indices in the feature-space.
Figure 04: Comparison of Accuracies of Classification Using Various Indices along with
Spectral Features
0.68

0.70

Kappa Accuracy
0.72
0.74

0.76

0.78

SpectralSpectral + NDVI

MLC

Spectral + NDWI

SVM

Spectral + BSI
Spectral + NDVI + NDWISpectral + NDVI + NDWI + BSI

Effect of Adding Textural Features to the Feature-Space
Kappa values were compared to study the impact of varying window sizes while preparing the
GLCM-based textural features which were added to the original spectral bands with thesequence shown in Figure 05. It was observed that SVM classification accuracy decreased
when opting for bigger window sizes while generating texture features. Whereas for MLC, asignificant decrement was observed when any of the texture features were added to thefeature-space. Seeing the results, it was decided to focus on 3x3 window size among all GLCM
variants in rest of the study. Further, as shown in Figure 06, all the texture features generated
with 3x3 window size were added one at a time to the spectral features and classified using thetwo classifiers. It was observed that with SVM, correlation feature produced best results
followed by mean and variance. Whereas with MLC, no improvement was seen in Kappa
values, in fact, variance features performed worst.
Figure 05: Comparison of Accuracies of Classification for the Case when GLCM-Based
Textural Features Derived at Various Window Sizes was added to Original Spectral
Features
0.30

0.40

Kappa Accuracy
0.50
0.60

Spectral

0.70

0.80

MLC

Spectral + GLCM 3x3

SVM

Spectral + GLCM 5x5Spectral + GLCM 7x7
Spectral + GLCM 9x9

Effect of Adding Both Spectral Indices and Textural Features to the Feature-Space
Having observed from Figure 06 that with SVM, correlation texture feature at 3x3 window size
performed best among all the texture features, this feature was added to the multispectral
bands along with various combinations of spectral indices in the composite. The results areshown in Figure 07. It is evident from the figure that with SVM, “Spectral + Correlation + NDVI”
performed best among all other feature-spaces with Kappa value of 0.794 (Figure 08). BSI wasagain proved to give a decreased accuracy whenever included in the composite. Again, MLC
was found to show no significant improvements with the added features in feature-space.
12Figure 06: Comparing Accuracies of Classification Using Various GLCM-Based Features
Derived at 3x3 Window Size Along with Spectral Features
0.50

0.55

0.60

Kappa Accuracy
0.65

0.70

0.75

0.80

SpectralSpectral + Correlation
Spectral + Mean
Spectral + Entropy
Spectral + Second Moment
Spectral + Homogeneity
Specral + Contrast
Spectral + Dissimilarity
Spectral + Variance

MLC
SVM

Figure 07: Comparing Accuracies of Classification Using Various GLCM-Based Features
Derived at 3x3 Window Size and Various Indices Along with Spectral Features
0.60

0.65

Kappa Accuracy
0.70

0.75

0.80

SpectralSpectral + Correlation + NDVISpectral + Correlation + NDWI

MLC

Spectral + Correlation + NDVI + NDWI

SVM

Spectral + Correlation + NDVI + BSI
Spectral + Correlation + NDVI + BSI + NDWISpectral + Correlation + BSI + NDWISpectral + Correlation + BSI

Figure 08: SVM Classification Output that Achieved the Highest Kappa Accuracy (0.794)
During the Study – Generated by Adding NDVI and Correlation Texture Feature with 3x3
Window size To the Composite of Spectral Bands

To evaluate the class-wise performance with varying feature-spaces, user’s and producer’s
accuracies of each class were plotted (as shown in Figure 09) from the best classification
results as examined from the three cases described above. For a classification to besatisfactory, it is desirable for any class plot to tend towards the top right corner of the graphs
shown in Figure 09. Overall, relatively better performing classes were Fine-River-Sand,
Coarse-River-Sand, Turbid-Water, Non-Turbid-Water, Sparse-Vegetation and DenseVegetation wherein both user's and producer's accuracies were found to be towards the higher
side for both SVM and MLC. Classes for which a notable amount of misclassification(omission/commission errors) was detected were Wet-River-Sand, Rocky-Area, Bare-Soil and
Built-Up. This was due to the spectral confusion occurring in case of channel sand bars withurban classes and bare soil found outside the channel (Gilvear et al., 2004). Spectral confusion
also arise by the development of vegetation over the inactive bars that can influence the opticalproperties of channel bar, although no or sparse vegetation cover indicates an active channelbar (Hooke and Yorke, 2011; Wang et al., 2016). However, Wet-River-Sand, Rocky-Area, BareSoil and Built-Up were found to be better classified by SVM when compared with MLC. It was13

observed that SVM with “Spectral + Correlation + NDVI” outperformed in the whole experimentbecause these classes were classified with improved accuracies.
Figure 09: Class-Wise Representation of User's and Producer's Accuracies from Various
Feature-Space Combinations
1.0

Fine River Sand
Producer's Accuracy

Producer's Accuracy

1.0

0.5

0.0

0.5
User's Accuracy

0.0

1.0
1.0

Wet River Sand
Producer's Accuracy

Producer's Accuracy

1.0

0.5

0.0

1.0

0.5
User's Accuracy

1.0

Turbid Water

0.5

1.0

0.0

0.5
User's Accuracy

1.0

1.0

Non-Turbid Water

Rocky-Area
Producer's Accuracy

Producer's Accuracy

0.5
User's Accuracy

0.0
0.0

0.5

0.0

0.5

0.0
0.0

1.0

0.5
User's Accuracy

1.0

0.0
1.0

Bare Soil
Producer's Accuracy

Producer's Accuracy

0.5

0.0

0.0

0.5

0.0

0.5
User's Accuracy

1.0

Sparse Vegetation

0.5

0.0
0.0

1.0

0.5
User's Accuracy

1.0

0.0
1.0

Dense Vegetation
Producer's Accuracy

Producer's Accuracy

Coarse River Sand

0.5

0.0

0.5
User's Accuracy

1.0

Built-up

0.5

0.0
0.0

0.5
User's Accuracy

1.0

0.0

14

0.5
User's Accuracy

1.0Conclusion
In this study, pixel-based semi-automated mapping of river sand was attempted by the meansof space-based remotely sensed imagery. The study showed that with the increasing
availability of high-spatial-resolution multispectral data, river sand planimetric extent can bereadily mapped by applying supervised image classification techniques like MLC and SVM,
SVM performing better among them. Moreover, accuracy of SVM classifier increases when
NDVI and correlation texture feature computed at 3x3 window size are added to the composite
of spectral bands while classification. The resultant planimetric spread of Fine-River-Sand,
Coarse-River-Sand and Wet-River-Sand in the study area was 67.3 ha, 209.5 ha and 189.9 ha,
respectively calculated with kappa value of 0.794. Challenge still lies in distinguishing WetRiver-Sand from Bare-Soil and Built-Up class categories, for which, it is suggested to include
contextual information in addition to the spectral and textural features.
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