DYNAMIC CLASSIFIER SYSTEM FOR HYPERSPECTRAL IMAGE CLASSIFICATION

D Bharath Bhushan, Rama Rao Nidamanuri

Department of Earth and Space Sciences, Department of Space, Govt. of India, Indian Institute of Space Science and Technology,

Thiruvananthapuram, India bharath. l l @iist.ac. in, rao@iist .ac . in

ABSTRACT

Multiple classifier system (MCS) is one of the effective strategies for hyperspectral image classification. Deploying different dimensionality reduction methods as the input data source to the MCS creates diversity among the base classifiers . The performance of the MCS is guaranteed when the base classifiers are accurate and diverse. Moreover the presence of the bad classifiers may negatively influence the performance of the MCS. In order to form a strong MCS, which are accurate as well as diverse, in this work the dynamic classifier system is developed. The dynamic classifier system selects the adaptive classifier from a pool of classifier for each dimensionality reduction method. The selected classifier relative to each dimensionality reduction method is further combined by different combination functions. Our experimental results on five multi-site hyperspectral images show the potential of dynamic classifier system to increase the classification accuracy significantly.

Index Terms- Hyperspectral image classification, multiple classifier system, dynamic classifier system, dimensionality reduction methods

1. INTRODUCTION

Classifier selection is an important task in hyperspectral image classification process. High dimensionality typical to hyperspectral images [ 1 ] further complicates the classifier selection and constraints the classification performance . A number of dimensionality reduction methods and classifiers are proposed in literature and their performances are competitive to each other in different aspects . The classification accuracy depends upon the subjective choice of dimensionality reduction methods and classifiers . Identifying the optimal pair of dimensionality reduction method and classifier is a tedious task. Numerous studies have addressed the suitability of dimensionality reduction

methods and classifiers for different applications [2] , [3 ] . Most of the studies, however, are application specific and the knowledge gained from these studies may not be applicable across different images. Multiple classifier system (MCS) has the capability to generalize the selection of classifiers and dimensionality reduction methods. The ability of the MCS to offer enhanced classification accuracy is governed by the diversity of classifiers and their ability to commit different types of errors [4] [5] . However, the composition classifiers in the MCS and hence the diversity among them is data and application specific . The utility of MCS in hyperspectral image analysis increase significantly if its architecture is adaptive to the various input data. This requires data based diversity creation in the MCS. Further, the extension of the MCS framework to permit dynamic selection of a pair of dimensionality reduction method and classifier enhances the capability of the MCS to produce classification results appropriate to the data at hand. We observe that the perceived differences in the performance of various classifiers relative various dimensionality reduction methods in principle have the potential to create data based diversity in the MCS. The objective of this work is to design a mechanism named as dynamic classifier system (DCS) to classify hyperspectral images by dynamically selecting an optimal classifier for each dimensionality reduction method in the MCS. The proposed DCS framework helps make MCS maintain diversity in the classification independent of the classifiers composition in the MCS while enhancing the overall classification performance . Further, we assessed the comparative performance of various trainable and non­trainable combination schemes on the performance of the proposed DCS.

2. METHODOLOGY

2.1 Dimensionality reduction methods and classifiers

Different dimensionality reduction methods provide diverse information to the base classifiers in the MCS. We used five popular dimensionality reduction methods : principal

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Table 1 : List of DCS based best classifier-dimensionality reduction method pair and overall accuracy estimates . Overall accuracy estimates from the MCS for reference.

Best classifier relative to dimensionality Best

H yperspectral reduction method classifier

MCS datasets

lCA PCA MNF

HyMAP MDC LDC SAM

ROSIS University ACE MDC SAM

ProSpecTIR SAM MDC SSM

ROSIS City of Pavia MDC ACE SAM

HYDICE MDC SSM SAM

component analysis (PCA), independent component analysis (ICA), minimum noise fraction (MNF), discrete wavelet transform based dimensionality reduction method (DWT­DR) and optimal band selection (OBS) as the input data sources for the base classifiers in the MCS. The base classifiers considered for the MCS design are : minimum distance classifier (MDC), spectral angle mapper (SAM), spectral similarity measure (SSM), linear discriminant classifier (LDC), adaptive coherence estimator (ACE), orthogonal subspace projection (OSP), and target constraint interference minimized filter (TCIMF).

2.2 Dynamic classifier system

In order to evolve at a stronger MCS and to satisfy the diversity requirement of MCS, we redesigned the classifier selection criterion in the MCS to select an optimal classifier relative to each dimensionality reduction method. We call this modified MCS framework as the dynamic classifier system. Dynamic classifier system is a ranking based approach, in which the individual classifiers are ranked according to a certain criterion and the top ranked classifiers are selected. We used Kappa coefficient and overall accuracy as the criteria to rank the classifiers in the MCS. The new MCS is thus formed with five classifiers, one for each dimensionality reduction method with input data from the five dimensionality reduction methods. The algorithmic frame work of dynamic classifier system is shown in Figure. 1 .

Let 1 be the input hyperspectral imagery, let D = {D1 , D2 , • . • , DM } be the set of M dimensionality

reduction methods, let C = {C1 , C2 , . • . , Cd be the set of L classifiers, let X, Y be the training and testing location index in the hyperspectral image, let N be the total number of testing samples, let E be the confusion matrix, where Eij represents number of samples belonging to ith class is classified into l class and let c be the number of classes. The algorithmic frame work of dynamic classifier system is given in Figure. 1

DWT-DR OBS accuracy accuracy

ACE SSM 92 .73 95 .32

SSM SSM 80.3 8 83 . 1 5

LDC SSM 9 1 .32 89 .99

SSM SSM 88 . 1 3 89 .94

ACE SSM 95.99 97.74

2.3 Classifier combination methods

The decision function values of the classifiers relative to dimensionality reduction methods are combined by various combination schemes to produce the fmal classified image. The combination schemes consists of non-trainable combination schemes : majority voting (MY), maximum (Max), minimum (Min), product (Prod), average (Avg), median rule (Med) and trainable combination schemes : support vector machine (SYM), maximum likelihood classifier (MLC). The non-trainable combination schemes have been extensively used in MCS due to its simplicity and robustness [6] . The classifiers decision values are stacked together as the one-dimensional vector and given as the input for the trainable combination scheme .

Input parameters : l, D, C,X, Y Output : Selected classifiers relative to each

dimensionality reduction method

DCS Algorithm :

Let S = { } be the empty set

For every D" i = 1 , 2, . . . , M Apply i'h

dimensionality reduction method (D, ) on the 1

Extract training and testing samples of transformed

image by D, For every Cj , j = 1 , 2, . . . , L

Train the classifier Cj Compute the confusion matrix E and calculate

c

L Emm the overall accuarcy 0u = "",mc::�,-l -­

N End for

k = arg max 0i/ , l = 1 , 2, . . . , L I

Update the classifier set S = S u {Cd End for

Figure 1 : Algorithmic representation of dynamic classifier system

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Table 2 : Overall classification accuracy of hyper spectral datasets obtained from various combination schemes

Hyperspectral Non-trainable combination scheme Trainable

combination scheme datasets

MV Max Min

HyMAP 97. 1 0 92. 1 1 92.23

ROSIS University 8 1 .0 1 78 .99 82.56

ProSpecTIR 93 .00 88 .76 90.64

ROSIS City of Pavia 89 . 8 1 86 .3 1 88 .62

HYDICE DC 97.94 94.48 94 .86

3. RESULTS AND DISCUSSION

Image classification experiments were conducted with five different airborne hyperspectral data sets namely HyMAP (Deldow, Germany), ROSIS (University of Pavia, City of Pavia), ProSpecTIR (Reno, USA), and HYDICE (Washington DC). The HyMAP imagery consists of argicultural land cover classes, and the remaining imagery consists of urban mixed environments. Table 1 shows the classifiers identified by the dynamic classifier system from the pool of classifiers relative to each dimensionality reduction method for the five different hyperspectral datasets . It is interesting to observe that different classifier was selected for each dimensionality reduction method and variation in the selected classifiers was observed across the different hyperspectral datasets . The classification results from the dynamic classifier system were compared with the results from the single best classifier, and with the typical MCS.

The classification results of MCS are obtained by two level­combinations . In the first level, the classifiers in the MCS are classified relative to each dimensionality reduction and combined with all the combination functions. In second level combination, the combined images with best combination function relative to each dimensionality reduction method are further combined by majority voting to produce final classified image. The classification accuracy of the single best classifier and the MCS are shown in Table 1 . Table 2 shows the results of the combination of outputs of optimal classifiers . When compared with the accuracy achieved by the single best classifier and the typical MCS, significant increase in accuracy can be observed with dynamic classifier system (results of the statistical significance test are not presented) . However, the increase in accuracy observed significantly depends upon the combination scheme used. For non-trainable combination scheme, the increase in classification accuracy for HyMAP, ROSIS University, ProSpecTJR ROSIS City of Pavia, and HYDICE are 5 .45%

Avg Prod Med SVM MLC

95 .40 94 . 82 98 . 1 8 99.23 99.6 1

85 .44 86 .30 79.83 92 .50 89 .06

93 .23 92 .79 92 .94 95 . 84 94. 1 5

88 . 1 8 89. l 1 90. l 8 92 .25 92 .86

98 .89 98 .9 1 98 .24 98 .70 98 .84

(median rule), 5 . 92% (product rule), 1 .9 1 % (average rule), 2 .05% (median rule), and 2 .92% (product rule) respectively over the single best classifier, while it were 2 . 86%, 3 . 1 5%, 3 .24%, 0 .24%, and 1 . 1 7% respectively over the MCS. When the classifiers decision function values were combined by trainable combination scheme (SVM and MLC), the classification accuracy increased further. For trainable combination scheme, the increase in classification accuracy for HyMAP, ROSIS University, ProSpecTIR, ROSIS City of Pavia, and HYDICE are 6 .8% (MLC), 12 . 12% (SVM), 4 .52% (SVM), 4 .73% (MLC), and 2 . 85% (MLC) respectively over the single best classifier, while it were 4.29%, 9 .3 5%, 5 . 85%, 2 .92%, and l . l 0% respectively over the MCS. There is no considerable variation among the performance of SVM and MLC combination schemes (see Table 2). The accuracy differences between trainable and non-trainable combination schemes are significant, indicating the importance of choosing proper combination scheme in the DCS. It can be seen that the difference in classification accuracy between trainable and non-trainable combinations schemes for the HYDICE is negligible. The potential of trainable combination scheme may due to the fact that its ability to handle different output distributions of classifiers' decision value [4] . The classified images by the proposed dynamic classifier system with best non-trainable and trainable combination function for five hyperspectral images are shown in Figure . 2 and Figure. 3 .

4. CONCLUSION

In this paper, a framework named as dynamic classifier system is introduced to dynamically select optimal classifier for each dimensionality reduction method for image classification by the MCS framework. The impact of selecting optimal classifiers and dimensionality reduction methods for improved hyperspectral image classification has been assessed with trainable and non-trainable combination schemes . Experimental results on five different hyperspectral datasets reveal that this framework increases the classification accuracy significantly over the single best

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classifier and the typical MCS. Further, results suggest that diversity in the MCS can be created by deploying input data from multiple dimensionality reduction methods.

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Figure 2 : Classified images obtained by the proposed DCS (with best non-trainable combination function) (a) ROSIS­University (b) ROSIS-City of Pavia (c) ProSpecTIR (d) HyMAP (e) HYDICE.

Figure 3 : The classified images by the proposed dynamic classifier system (best trainable combination function) (a) ROSIS-University (b) ROSIS-City of Pavia ( c) ProSpecTIR (d) HyMAP (e) HYDICE.

5. ACKNOWLEDGEMENT

The authors would like to thank Prof. Gamba, University of Pavia, Italy, for providing the ROSIS Hyperspectral datasets and ground truth images. First author sincerely thanks IEEE­GARSS for providing the travel grant to attend the IGARSS-20 1 3 .

[2]

[3]

[4]

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