Evaluation of Image Classification Algorithms on Hyperion and ASTER

Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. https://doi.org/10.1007/s40010-017-0454-6

RESEARCH ARTICLE

Evaluation of Image Classification Algorithms on Hyperion and ASTER Data for Land Cover Classification Deepika Mann1 • P. K. Joshi2

Received: 20 June 2017 / Revised: 15 August 2017 / Accepted: 10 September 2017  The National Academy of Sciences, India 2017

Abstract Land use and land cover (LULC) mapping is one of the widely adopted applications of satellite data. With the advent of new technologies and sensor improvements, many classification algorithms are being developed. However, there are rarely studies on comparison of these classifiers using identical classification scheme and training data over different sensors and their products. In this article, we tested the effect of improved spectral and spatial resolution on classification performance of ASTER data (15 m), Hyperion data (30 m) and their fused product (15 m). For this purpose, we have used five supervised classification algorithms -three spatial classifiers, namely, Maximum Likelihood (MLC), Support Vector Machines (SVM), Artificial Neural Network (ANN) and two spectral classifiers, namely, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID). The performance of image classification algorithms was assessed using overall accuracy (OA) and kappa coefficient. MLC and SVM performed the best on all the three datasets. OA and kappa values for almost all the classifiers were comparable for higher spatial resolution ASTER and fused product and were higher by nearly 10% than that for higher spectral resolution Hyperion data. Keywords Multispectral  Hyper spectral  Supervised classification  Fusion  Accuracy

& P. K. Joshi [email protected] 1

Department of Civil Engineering, Shiv Nadar University, Dadri, UP 201314, India

2

School of Environmental Sciences, Jawaharlal Nehru University, New Delhi 110 067, India

1 Introduction Land use land cover (LULC) classification and assessment is one of the core applications of remote sensing data [1]. The advances in sensor technologies have resulted in substantial improvement in the spatial and spectral resolution of the satellite data. Spatial resolution is defined as the degree of detailing in an image and spectral resolution as the width of the bands in which the sensor measures the incoming radiance [2]. Multispectral system has higher spatial resolution and collects data in less than ten bands whereas hyper spectral system has higher spectral resolution and collects data in hundreds of bands. Image fusion is the process of combining two or more images to get an image with higher information content [3]. Fusion of hyper spectral data with panchromatic or multispectral data results in higher classification accuracy due to improvement in the spatial resolution. Nejad et al. [4] tested the classification performance of Support Vector Machine (SVM) on original and pan-sharpened Hyperion data for 14 classes and have reported a 14.7% increase in the classification accuracy on an average. Licciardi et al. [5] also reported an increase in the overall accuracy of SVM classifier from 90 to 97% on the original Compact High-Resolution Imaging Spectrometer Part Mission Project for OnBoard Autonomy (CHRIS-PROBA) hyper spectral image and its fusion product of pan-sharpening and spectral unmixing. Accurate image classification is important for effective information extraction from the remotely sensed data. Many classification algorithms have been developed based on parametric, non-parametric, spatial information, and spectral information for mapping LULC from satellite data and have been compared for classification performance. However it has been confined only to comparing new

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D. Mann, P. K. Joshi

Fig. 1 Location of the study area

algorithm with a traditional one like maximum likelihood classifier [6–8] or comparing two to three of the new ones [9]. Some studies have explored the effect of various fusion techniques on classification accuracies [10–13] but the effect of improvement in spatial and spectral resolution on classification algorithms has rarely been studied with different sensors and the same classification scheme. Such studies are even more limited for pan-sharpened hyperspectral data. This study aims to fill these gaps by using some of the key classification algorithms on multispectral and hyper-spectral remote sensing data and their fused product. The two folds objective of this study are to (1) compare various ready to use spatial and spectral classifiers on the same as well as different satellite sensors and (2) to

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study the effect of improvement in spatial and spectral resolution on classification algorithms. 1.1 Study Area The study area spans over 21 km2 and falls in the southern part of Delhi, the capital city of India. The spatial extent of the study area is from 28.52–28.56N to 77.18–77.23E. The area experiences hot and dry summers with maximum temperature as high as 47 C and cold winters with minimum temperature 3 C. It is a metropolitan city covered mostly with high and low density built up areas, some ridge forests, gardens and industrial areas. A map showing location of the study area is presented in Fig. 1. Delhi

Evaluation of Image Classification Algorithms on Hyperion and ASTER Data for Land Cover…

being the capital city of India has extensively been studied for LULC mapping and change detection. 1.2 Datasets and Pre-processing The Hyperion (HyS, 20th February, 2010) data consisting of 220 bands (400–2500 nm) with 30 m spatial resolution and VNIR (Visible-Near Infrared) bands of ASTER(Advanced Space borne Thermal Emission and Reflection Radiometer) multispectral (MS, 5th March, 2010) data with 15 m spatial resolution, covering southern and central part of Delhi were acquired from the USGS (United States Geological Survey) Earth Explorer. The radiometrically corrected (L-1R) Hyperion dataset and Terrain corrected (L-1T) ASTER data were used for this study. Hyperion collects data at 12 bit radiometric resolution but is available for use at 16 bit radiometric resolution. The preprocessing of HyS dataset included bad band removal, destriping and atmospheric correction using FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) and was reduced to 106 spectral bands. The spectral subset of ASTER dataset with VNIR bands was used in this study for differentiation of the spatial resolution among the datasets, and hence the cross-talk correction of the ASTER data was not performed as it is done for Shortwave Infrared (SWIR) bands in the data. ASTER collects VNIR, SWIR and Thermal Infrared (TIR) data in 8, 12, 16 bit radiometric resolution respectively. The atmospheric correction of ASTER data was performed using FLAASH. The reflectance file thus obtained had a radiometric resolution of 16 bit. Thus, radiometry among the Hyperion and ASTER data was maintained at 16 bit. The two datasets were then accurately geo-registered and co-registered with RMSE less than 0.4 for performing image fusion. Gram Schmidt (GS) pan sharpening algorithm was chosen after literature review as it is reportedly more accurate and does not limit the number of bands used in the fusion process. During image fusion when we replace one of the bands of Hyperion with ASTER band due care needs to be taken to match the histogram of both the datasets, which was performed manually. For multispectral image fusion, the lower

resolution image band with the wavelength closest to the higher resolution image band should be replaced. This was carefully done while performing fusion of Hyperion with ASTER. Blue, Green, Red, NIR and SWIR bands of the hyperion were fused separately with the corresponding ASTER bands and then were stacked together to form the fusion product. The final fusion product has spatial resolution of ASTER data i.e. 15 m and preserves the spectral resolution of Hyperion in 106 bands (renamed consecutively from 1 to 106). The radiometry of the fusion product was also preserved, however color saturation was observed for vegetation features. The bands which were used for the fusion process are mentioned in Table 1 and other details can be found in [14]. A final subset of the study area was extracted from all the three datasets for performing classification. All the pre-processing and classification is done in ENVI 4.7 (Environment for Visualizing Images).

2 Methodology Based on a combination of visual interpretation from ASTER image and spectral diversity available in Hyperion image, the study area was classified into seven LULC classes. The description of the LULC classes, namely, water, grass, trees, dense built-up, sparse built-up, open space and shrubs, is given in Table 2. The training and testing samples were collected from ASTER data (15 m) as it has higher spatial resolution as compared to Hyperion (30 m). These were further validated from the Google Earth images. The training samples were kept the same for comparing classification accuracies over different datasets to avoid the human error associated with collecting training samples differently. It is recommended that the testing sample should be in the range of 10–30% of the total samples to train the data. Hence, we divided the data into training and testing samples as 70 and 30% of the total samples respectively. Five popular classification techniques including three spatial classifiers [32], namely, Maximum Likelihood (parametric), Support Vector Machines (non-parametric),

Table 1 Band combinations used for image fusion Hyperion

ASTER

Middle range HyS band

Bands

Wavelength (nm)

Band

Wavelength (nm)

Band

Wavelength (nm)

Blue: 1–6

467.52–518.39

Green: 1

556

4

498.04

Green: 7–14

528.57–599.80

Green: 1

556

10

599.09

Red:15–28

609.97–742.25

Red: 2

661

21

671.02

NIR: 29–34

752.43–803.30

NIR: 3

807

31

772.78

NIR ?SWIR: 35–106

813.48–1780.09

NIR: 3

807

81

1527.93

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D. Mann, P. K. Joshi Table 2 Description the LULC classes LULC Class

Description

Water

Water bodies like rivers, lakes, ponds, canals etc

Grass

Areas dominated by thick grass like golf links, parks

Trees

Areas with tall and dense trees

Dense built-up

Tightly packed built-up areas with no space or any other land use in between

Sparse built-up

Built-up areas with some vegetation or trees in between them

Open space

Land where soil is exposed and no vegetation grows, could be because of incomplete construction also

Shrubs

Areas with sparse vegetation mixed with exposed soil

Artificial Neural Network (non-parametric), and two spectral classifiers [32], namely, Spectral Angle Mapper (non-parametric) and Spectral Information Divergence (non-parametric) are applied to the ASTER (15 m), Hyperion (30 m) and fused product (15 m) of ASTER and Hyperion. A brief description of the classification algorithms is presented in the following section. Maximum Likelihood Classification MLC is amongst the most popular and extensively used parametric supervised classification algorithm. It is based on the Bayes theorem and makes two principal assumptions that the data is normally distributed and the statistical parameters associated with the training data are true representative of the associated LULC class [15]. It is described as consisting of a probability model that presents a probability measure of each pixel of belonging to a particular class rather than providing a class value (Sharma et al. 2013). For Fc predefined classes, the algorithm to calculate the probability or likelihood (P) of unknown measurement vector ‘X’ belonging to any one of the classes is established using the Bayesian Eq. 1 [16]. P ¼ lnðac Þ  ½0:5 lnðjcovc jÞ  ½0:5ðX  Fc ÞT ðcovc  1ÞðX  Fc Þ ‘X’ is assigned to the class which has its maximum likelihood of the belonging. MLC performs better with a normally distributed data, however, for skewed distributions, the classification results may or may not be appreciable. Support Vector Machines: it is a non-parametric supervised classification algorithm derived from the statistical learning theory, which was developed in 1960s for non-parametric estimation of the dependency with the finite data [17]. SVM, in principle, is a binary classifier but it is modified to solve a multiclass problem by a combination of multiple binary SVM classifiers. SVM separates the predefined classes Fc with the optimal hyperplane (OH), which is defined as the separating hyperplane, which maximizes the distance between the classes ‘Fc’. The principle is briefly

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explained below by means of supporting equations. Let us assume that the training data is denoted by x 2 Rn ; y 2 fþ1; 1g

ðx1 ; y1 Þ; . . .; ðxk ; yk Þ;

A hyperplane can linearly separate the training data, if the following two conditions are satisfied by ‘w’ (a vector) and ‘b’ (a scalar) [15]: w  xi þ b  þ 1

for ally ¼ þ1; where i ¼ 1 to k

w  xi þ b  þ 1

for ally ¼ 1; where i ¼ 1 to k

The constraint that is to be met for achieving the hyperplane for separating two classes completely can be represented by combining equation [18]. y i ð w  x i þ bÞ  1  0 In order to find the optimal hyperplane SVM uses the Lagrange multiplier along with quadratic programming forms [19]. For non-linearly separable classes or multiclass problems, kernel functions are used which converts the input vector ‘x’ into a constructed space of n dimensions from a feature space [15]. The classification accuracy using SVM classifier depends largely on the selection of the parameters and the kernel function [20]. We have used radial basis function as the kernel function, which is the most common and effective, for this study. Other parameters like gamma and penalty parameter were also tested with different values and the combination which gave the highest classification accuracy is reported in Table 3. Artificial Neural Network Artificial NNs are similar to the biological NNs. It is a non-parametric supervised CA. The multi layer perceptron feed forward neural network based on Richards [21] and implemented in ENVI has been used for this study. The mechanism of working of the NNs is well documented by Shafri et al. [22] and Zhou et al. [23]. It has a number of parameters that can be varied, for instance, the number of hidden layers, the activation function, number of iterations etc. and has a documented effect on the classification accuracy [23]. We have tried various combination of different parameters based on Zhou

Evaluation of Image Classification Algorithms on Hyperion and ASTER Data for Land Cover… Table 3 Classification algorithm parameter setup Classification algorithm

Abbreviation

Parameters Name

Value Default

Maximum likelihood

MLC

Threshold value

Support vector machines

SVM

Kernel function

Radial basis function

Gamma

0.142

Penalty parameter

500

Spectral angle mapper

SAM

Maximum angle in radians

1.0

Spectral information divergence

SID

Maximum divergence threshold

1.0

Neural network

NN

Training rate No. of training iterations

0.3 3000

Others

Default

et al. [23] and the one with the highest accuracy is reported in Table 3. Spectral Angle Mapper SAM is a non-parametric supervised spectral classifier. The algorithm matches the pixels to the reference spectra using an n-dimensional angle. It calculates the angle between two spectra, treating them as vectors in n-dimensional space where n is the number of bands and thereby determines the spectral similarity between them. Pixels that are away from the specified maximum angle (MA) in radians remain unclassified [22]. For SAM classification we had tried various values for the MA parameter starting from 0.01 to 2.0 and the one with the highest accuracy is mentioned in the Table 3. Spectral Information Divergence SID is a non-parametric supervised spectral classifier that uses the divergence measure to match the pixels to the reference spectra. Pixels remain unclassified if the divergence measurement is greater than the specified divergence threshold in radians. Different values of the divergence threshold parameter were tried ranging from 0.01 to 2.0 and the one with maximum accuracy is reported in the Table 3. 2.1 Training Sample Training samples were collected by selecting a small group of homogenous pixels followed by growing the region based on similar properties. These were selected based on image interpretation and analyzing spectral properties of every class from the Hyperion data. The training sample size was so chosen that it is representative of the classes and is not too small and not too large. Although large training sample size is more representative of the class, a small training sample size is advisable for strategic reasons. Training sample size is often suggested to be no less than 10 times the number of spectral bands used for classification. This is usually recommended for classifiers like MLC that need fewer parameters to be evaluated. Table 4 provides the training samples for the study area.

Table 4 Number of training and testing samples for each class Class names

Training pixels

Testing pixels

Water

70

29

Grass

173

76

Trees

150

56

Dense built-up

339

178

Sparse built up

308

167

Open space

81

53

Shrubs

98

48

Total

1219

607

68%

32%

Class separability of each class in all the three datasets was checked using Transformed Divergence (TD). The TD test works on the principle that exponentially decreasing weights are assigned to the increasing distance between the classes. Classes are expected to have good separability if TD values are in the range 1.8–2.0. 2.2 Testing Sample We kept 30% of the total training samples separate as test samples (by simple random sampling) from 15 m ASTER data as well as simultaneous assessment of finer resolution Google earth imagery based on the prior knowledge of the study area. Each test sample also contains more than 30% of the training sample for each class. 2.3 Classification Process Using the collected training samples, above mentioned classification algorithms were applied on all the three datasets. For ASTER data, NIR, Red and Green bands were used, whereas for Hyperion and Fused product principal component analysis (PCA) was performed and classification performance was tested with 4, 5, 6 and 10 PCs to test

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if increasing the number of PCs affected the classification accuracy. MLC does not require many parameters, so classification with default parameters gave satisfactory results. However, other classification algorithms (SVM, NN, SAM and SID) require certain optimal parameters to perform well, which vary with data and the area of study. Hence, after fine-tuning and trying different values for various parameters, the one’s which gave highest classification accuracies are reported. 2.4 Accuracy Assessment Accuracy assessment was done to measure the performance of various classification algorithms on all the three data sets. It was done using confusion matrix and kappa statistics. LULC information of the collected testing samples was compared with that of the corresponding location in the classified map and a confusion matrix was generated which expressed accuracies as User’s, Producer’s, Overall Accuracies (OA), and Kappa Coefficient was also reported [24, 25].

3 Results and Discussion Class Separability Class separability assessment was done using the TD test, which was performed in ENVI. The TD values for most of the classes ranged between 1.8 and 2.0 in all the three datasets which suggests good separability, except for that between dense-sparse built-up and shrubssparse built-up, for which it showed moderate separability. The TD values of all the classes in the three datasets are mentioned in the Table 5. The lower TD values for dense, sparse built-up and shrubs, range from 1.294 to 1.634 in ASTER and Hyperion data. This suggests less separability among the classes. However, the lowest TD values for these classes in the fused data are between 1.612 and 1.689, which shows that the class separability is improved in the fused data. The results of the classification using various algorithms are shown in Figs. 2 and 3 shows a closer look at a part of the study area to visualize the classification results using various classification algorithms. Accuracy assessment of classification performance using MLC, SVM, NN, SAM and SID was done for the quantitative analysis. The results of the accuracy

Table 5 Transformed divergence for class separability Class

Wt

Gr

Tr

DBu

SBu

Os

Sh

ASTER Wt

2.000

Gr

1.999

1.999

1.949

2.000

1.998

1.967

1.999

1.999

1.999

1.998

Tr

1.999

DBu SBu

1.991

1.999

1.969

1.600

1.998 1.998

1.997 1.294

Os

1.934

Sh Hyperion Wt

2.000

Gr

1.990

1.999

1.925

2.000

1.994

1.974

1.997

1.973

1.948

1.900

Tr

1.999

DBu

1.711

1.990

1.313

1.574

1.995

1.997

1.982

1.634

SBu Os

1.829

Sh Fused Wt Gr Tr DBu

2.000

1.999

1.999

1.905

1.999

1.999

1.990

1.999

1.999

1.999

1.998

1.999

1.992 1.689

1.999 1.998

1.965 1.998

1.999

1.612

SBu Os Sh Wt water, Gr grass, Tr trees, DBu dense built-up, SBu sparse built-up, Os open space, Sh shrubs

123

1.920

Evaluation of Image Classification Algorithms on Hyperion and ASTER Data for Land Cover…

Fig. 2 Classification results of MLC, SVM, NN, SAM and SID for ASTER denoted by ‘A’, Hyperion denoted by ‘H’ and Fused Image denoted by ‘F’

assessment were generated in the form of a confusion matrix and OA and Kappa coefficient values are reported for all the classification algorithms used on ASTER, Hyperion and the fused data (Table 6), and a graph representing the same is shown in Fig. 4. A comparison of the Producer Accuracy (PA) and User Accuracy (UA) is shown in Table 7 to compare the classification of individual classes. 3.1 Classification Accuracy and the Effect of Improvement in Spatial and Spectral Resolution ASTER Data The data with 15 m spatial resolution and spectral information in 3 bands covering VNIR spectrum has shown maximum classification accuracy using MLC algorithm with OA 93.14% and kappa as 0.9176. SVM has also performed quite well with OA only 1% less than MLC

as 92.42% and kappa as 0.9049, followed by NN with OA and kappa as 87.15% and 0.8369 respectively. Whereas, the spectral classifiers SAM with OA 81.59% and kappa as 0.7688 as well as SID with least OA of 41.68% and kappa as 0.3109 have not performed well for this case study. Hyperion Data It has lower spatial resolution of 30 m among the three datasets used in this study but had spectral information in 106 bands covering VNIR-SWIR spectrum range. For Hyperion data, SVM outperformed all other classifiers with an OA of 88.14% and kappa of 0.8508, followed by MLC with OA and kappa values as 86.16% and 0.8272 respectively. NN gave OA of 81.05% and kappa as 0.7619. Similar to the case of ASTER data, spectral classifiers did not perform well for Hyperion data as well with OA and kappa for SAM as 70.68% and 0.6428 respectively, and that for SID as 59.14% and 0.5144 respectively. However, OA values for SID showed an increase of approximately 20% when compared to that for

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Fig. 3 Classification results of MLC, SVM, NN, SAM and SID for a part of the study area on ASTER denoted by ‘A’, Hyperion denoted by ‘H’ and Fused Image denoted by ‘F’ Table 6 Comparison of classification accuracies of CAs on ASTER, Hyperion and Fused data ASTER

Hyperion

Fused

OA

Kappa

OA

Kappa

OA

Kappa

MLC

93.4102

0.9176

86.1614

0.8272

93.575

0.9195

SVM

92.4217

0.9049

88.1384

0.8508

93.575

0.9193

NN

87.1499

0.8369

81.0544

0.7619

91.7628

0.8971

SAM

81.5486

0.7688

70.6755

0.6428

78.2537

0.7315

SID

41.6804

0.3109

59.1433

0.5144

73.4761

0.6733

ASTER data. This could be attributed to the fact that SID is a spectral classifier and Hyperion has a higher spectral resolution than ASTER. Fused Product Fusion product with spatial resolution of ASTER (15 m) and spectral resolution of Hyperion in 106 bands has shown an overall better classification performance amongst all the datasets with the three spatial classifiers, namely, MLC, SVM and NN having OA and

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kappa values more than 90% and 0.90 respectively. The spectral classifiers have not performed well in comparison to the spatial classifiers with SAM producing OA and kappa as 78.25% and 0.7315 respectively and that for SID as 73.48% and 0.6733 respectively. However SID has shown a significant improvement of approximately 33 and 14% when compared to ASTER and Hyperion, respectively.

Evaluation of Image Classification Algorithms on Hyperion and ASTER Data for Land Cover…

Fig. 4 Comparison of classification accuracies using OA and Kappa Coefficient

In order to provide an insight into the improvement in classification accuracy of individual classes, we compared the producer accuracy (PA) and user accuracy (UA) of the seven classes for the three datasets presented in Table 7. Since MLC and SVM showed the highest OA and kappa, PA and UA of only these two classification algorithms are analysed for the seven classes. For all the classes, except Dense Built-up, Hyperion gave the least PA and UA, owing to its lower spatial resolution of 30 m. MLC using fused product gave highest PA and UA of 97.19 and 89.64% respectively. However, in SVM classification, both Hyperion and fused product gave equally good results and performed better than the ASTER data. Classification of grass using MLC and SVM produced approximately 100% PA and UA on both ASTER and the fused data. For classification of open space, fused data gave highest PA and UA for both MLC and SVM followed by ASTER and then Hyperion. For sparse built-up, fused data outperformed Hyperion by approximately 3% in PA and [ 1% in UA with PA on fused data 83.83, 84.43% respectively for MLC, SVM and OA as 93.96 and 95.27% respectively. However, the values for fused data were comparable with that of the ASTER data. Fused product increased the PA for shrubs from 43.75% in ASTER and Hyperion to 91.67% for MLC and also gave the highest PA and UA for other two datasets. Water is best classified on using ASTER data with nearly 100% PA and UA closely followed by fused data and then Hyperion. Classification of trees also follows the same trend. We can conclude that the PA and UA for almost all the classes has significantly

improved when using fused data as compared to Hyperion but did not show significant improvement over ASTER data. With the advent of newer and advanced remote sensing sensors and image processing techniques, the scientists have a lot of choices of satellite data for accurate mapping and assessment of natural resources. At present, when lot many multispectral and hyper spectral sensors are available and efforts for increasing spatial resolutions, it is important to evaluate the sensor systems and their spectral regions for discrimination of LULC features. Though the classification accuracy is factor of spatial details captured by the satellite data but the wavelength regions with higher overlap decrease the classification accuracy [26]. The spectral channels with smaller overlap should be combined and selected for an efficient classification [27–29]. The extent of intermixing of features depends upon many other factors, viz. spectral response of the particular class, spectral, spatial and radiometric resolution of the sensor [26]. Oza and Sharma [30] has shown that the visible and near infrared bands in combination with middle infrared band enhances dimensionality, spectral separability and classification accuracy of the data. Roy et al. [31] had done a comparative study of IRS-1A LISS II, Landsat-TM and SPOT-1, and reported that Landsat-TM had shown improved separability of vegetation type due to higher spectral resolution. Such type of analysis is due for hyper spectral and high spatial resolution remote sensing data.

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D. Mann, P. K. Joshi Table 7 Comparison of class-wise Producer Accuracy (PA) and User Accuracy (UA) Class

MLC

SVM

ANN

SAM

SID

PA

UA

Ast

Hyp

Fused

Ast

Hyp

Fused

Wt

96.55

82.76

89.66

100

100

100

Gr

100

98.68

100

100

98.68

100

Tr

100

94.64

100

100

60.92

100

DBu

95.51

96.63

97.19

88.08

88.66

89.64

SBu

83.23

76.05

83.83

95.21

92.70

93.96

Os

96.23

96.23

100

94.44

87.93

94.64

Sh OA=

43.75 93.41

43.75 86.16

91.67 93.58

87.04 0.92

67.74 0.83

86.27 0.92

Wt

100

89.66

100

100

100

100

Gr

100

93.42

98.68

100

100

100

Tr

100

100

98.21

100

64.37

98.21

DBu

97.19

100

98.31

84.80

85.99

86.63

SBu

80.24

81.44

84.43

93.71

94.44

95.27

Os

92.45

86.79

92.45

100

100

100

Sh

91.67

45.83

91.67

88.00

84.62

91.67

OA=

92.42

88.14

93.58

0.91

0.85

0.92

Wt

100

89.66

100

100

100

90.63

Gr

94.74

96.05

98.68

98.63

100

100.00

Tr

100

100

98.21

91.80

65.12

94.83

DBu

97.75

62.36

94.38

80.18

93.28

87.50

SBu

77.84

96.41

80.84

82.80

67.93

94.41

Os Sh

86.79 45.83

88.68 37.50

100.00 87.50

97.87 95.65

100 94.74

88.33 89.36

OA=

87.15

81.05

91.76

0.84

0.76

0.90

Wt

100

89.66

100

100

41.94

46.77

Gr

88.16

100

100

85.90

93.83

92.68

Tr

80.36

100

92.86

84.91

58.95

92.86

DBu

96.63

75.28

85.96

74.46

79.29

75.37

SBu

61.08

40.72

54.49

93.58

63.55

83.49

Os

62.26

100

100

84.62

71.62

85.48

Sh

97.92

33.33

43.75

69.12

94.12

84.00

OA=

81.55

70.68

78.25

0.77

0.64

0.73

Wt

3.45

89.66

100

3.45

26.26

38.16

Gr

72.37

64.47

90.79

67.90

92.45

100

Tr

83.93

78.57

66.07

47.96

47.83

67.27

DBu

37.64

63.48

87.08

69.07

81.29

71.10

SBu

34.73

39.52

46.11

59.18

75

86.52

Os Sh

7.55 43.75

86.79 31.25

94.34 60.42

2.88 32.31

50.55 33.33

72.46 93.55

OA=

41.68

59.14

73.48

0.31

0.51

0.67

4 Conclusion The study tested the effect of improvement in spatial and spectral resolution on classification accuracy of ASTER, Hyperion and their fusion product using three spatial and two spectral classifiers, out of which MLC is parametric

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K=

K=

K=

K=

K=

and others are non-parametric. The comparison of the CAs shows that the spatial classifiers performed better than the spectral classifiers over all the three datasets. MLC and SVM gave highest OA and kappa values for the three datasets. However, it cannot be conclusively said that one of these two performed better in the present study.

Evaluation of Image Classification Algorithms on Hyperion and ASTER Data for Land Cover…

In terms of improvement in spatial and spectral resolution, classification algorithms on fused data have performed equally well on the ASTER data with the same spatial but lower spectral resolution. However, when comparing classification performance of classification algorithms on fused data with the Hyperion data of same spectral but lower spatial resolution, all the classification algorithms have performed better for the fused data with approximately 10% higher OA for almost all the classifiers. Hence, it could be concluded that the improvement in spatial resolution leads to better classification performance than improvement in the spectral resolution. These aspects need to be further researched and documented. Acknowledgement Authors are thankful to the guest editors, anonymous reviewers and the editorial board of the journal for providing constructive comments and recommendations on earlier version of the manuscript. Authors acknowledge the support of the Ministry of Science & Technology, Department of Space & Technology, Big Data Initiatives Division (No. BDID/01/23/2014-HSRS) and JNU UPOE-II (ID: 300). DM is thankful to Shiv Nadar University for the technical infrastructure and PhD fellowship for research support. PKJ is thankful to DST-PURSE of Jawaharlal Nehru University for research support.

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13.

14.

15.

16. 17. 18.

19. 20.

21. 22.

References 23. 1. Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201 2. Munechika CK, Warnick JS, Salvaggio C, Schott JR (1993) Resolution enhancement of multi-spectral image data to improve classification accuracy. Photogramm Eng Remote Sensing 59(1):67–72 3. Pohl C, Van Genderen JL (1998) Multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens 19(5):823–854 4. Nejad AM, Ghassemian H, Mirzapour F (2015) GLCM texture features efficiency assessment of Pan-sharpened hyperspectral image classification for residential and industrial regions in Southern Tehran. J Geomat Sci Technol 5(1):55–64 5. Licciardi GA, Villa A, Khan MM, Chanussot J (2012) Image fusion and spectral unmixing of hyperspectral images for spatial improvement of classification maps. In: Geoscience and remote sensing symposium (IGARSS), 2012 IEEE International (pp 7290–7293) 6. Gong P, Howarth PJ (1992) Land-use classification of SPOT HRV data using a cover-frequency method. Int J Remote Sens 13:1459–1471 7. Shackelford AK, Davis CH (2003) A hierarchical fuzzy classification approach for high resolution multispectral data over urban areas. IEEE Trans Geosci Remote Sens 41:1920–1932 8. Pu RL, Landry S, Yu Q (2011) Object-based detailed land-cover classification with high spatial resolution IKONOS imagery. Int J Remote Sens 32:3285–3308 9. Tzeng YC, Fan KT, Chen KS (2009) An adaptive thresholding multiple classifiers system for remote sensing image classification. Photogramm Eng Remote Sens 75:679–687 10. Amarsaikhan D, Douglas T (2004) Data fusion and multisource image classification. Int J Remote Sens 25(17):3529–3539 11. Colditz RR, Wehrmann T, Bachmann M, Steinnocher K, Schmidt M, Strunz G, Dech S (2006) Influence of image fusion

24.

25. 26.

27.

28.

29.

30.

31.

32.

approaches on classification accuracy: a case study. Int J Remote Sens 27(15):3311–3335 Nikolakopoulos KG (2008) Comparison of nine fusion techniques for very high resolution data. Photogramm Eng Remote Sens 74(5):647–659 Taylor S, Kumar L, Reid N (2010) Mapping Lantana camara: accuracy comparison of various fusion techniques. Photogramm Eng Remote Sens 76(6):691–700 Mann D, Kushwaha A, Joshi PK (2017) Fusion of EO-1 Hyperion and Terra ASTER using Gram Schmidt image sharpening algorithm. Geocarto International (communicated) Kumar L, Sinha P, Taylor S (2014) Improving image classification in a complex wetland ecosystem through image fusion techniques. J Appl Remote Sens 8(1):083616 Erdas Inc. (1999) Erdas field guide. Erdas Inc., Atlanta, p 344 Vapnik VN (1995) The nature of statistical learning theory. Springer, New York, p 17 Szuster BW, Chen Q, Borger M (2011) A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones. Appl Geogr 31(2):525–532 Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26(5):1007–1011 Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: the case study of Beer Sheva, Israel. Remote Sens Environ 80(2):233–240 Richards JA (1999) Remote sensing digital image analysis. Springer, Berlin, p 240 Shafri HZM, Suhaili A, Manso S (2007) The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis. J Comput Sci 3(6):419–423 Zhou HC, Peng Y, Liang GH (2008) The research of monthly discharge predictor corrector model based on wavelet decomposition. Water Resour Manag 22:217–227 Congalton RG, Oderwald RG, Mead RA (1983) Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. Photogramm Eng Remote Sens 49(12):1671–1678 CongaltonRG Green K (2008) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press, Boca Raton Joshi PK, Gupta B, Roy PS (2008) Spectral evaluation of vegetation features using multi-satellite sensor system (Terra Aster, Landsat ETM ? and IRS 1D LISS III) in man-made and natural landscape. Sens Rev 28(1):52–61 Leckie DG, Teillet PM, Ostaff DP, Fedosejevs G (1988) Sensor band selection for detecting current defoliation caused by the spruce budworm. Remote Sens Environ 26(1):31–36 Ma Z, Olson C (1989) A measurement of spectral overlapping among cover types. Photogramm Eng Remote Sens 55(10):1441–1444 Decker AG, Malthus TJ, Wijnen MM, Seyhan E (1992) The effect of spectral bandwidth and positioning on the spectral signature analysis of inland waters. Remote Sens Environ 41(2/ 3):211–225 Oza MP, Sharma SA (1990) A comparative study of supervised classifiers on a subscene in Junagadh district of Gujarat. J Indian Soc Remote Sens 18:18–24 Roy PS, Ranganath BK, Vohra TPS, Diwakar PG (1988) Analysis of IRS-1A LISS II multispectral digital data for forest type discrimination and its comparison with Landsat-TM and SPOT-1. In: Proceedings of national seminaron Indian remote sensing satellite-1A (IRS-1A) mission and its application potential, Hyderabad Ghamisi P, Plaza J, Chen Y, Li J, Plaza AJ (2017) Advanced spectral classifiers for hyperspectral images: a review. IEEE Geosci Remote Sens Mag 5(1):8–32

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