STRATIFIED SAMPLING METHOD BASED TRAINING PIXELS SELECTION FOR HYPER SPECTRAL REMOTE SENSING IMAGE CLASSIFICATION

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1 Volume 117 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu STRATIFIED SAMPLING METHOD BASED TRAINING PIXELS SELECTION FOR HYPER SPECTRAL REMOTE SENSING IMAGE CLASSIFICATION 1 Radhesyam Vaddi, 2 M Prabukumar 1 Research Scholar, School of Information Technology Engineering (SITE), VIT University, Vellore , India & Department of Information Technology, V. R. Siddhartha Engineering College, Vijayawada 2 School of Information Technology Engineering (SITE), VIT University Vellore , India 1 syam.radhe@gmail.com, 2 mprabukumar@vit.ac.in Abstract: Hyper Spectral Image (HSI) classification is the fundamental tasks in remote sensing applications. In order to improve classification accuracy several attempts were made. Utilization of complete spatial information is the key aspect in the classification process. This paper proposes an approach for HSI classification using Support vector machine (SVM), 3Dimentional discrete wavelet transform (3D-DWT) with Stratified Random sampling. This is as extension to the method using SVM and 3D-DWT. The results are validated across standard data sets shows promising results on par with base line result. Keywords: Classification,HSI,SVM,3D-DWT 1. Introduction Hyper Spectral Image (HSI) captures reflectance values over a wide range of electromagnetic spectrum for each pixel in the image. This rich spectral information potentially provides information useful for image classification. HSI classification plays an important role in many remote-sensing applications, like environmental mapping, crop analysis, plant and mineral identification, and abundance estimation. Due to this the problem is become more interesting [1]. The Problem of classification in Hyper spectral images is most challenging because of its richness in dimensionality (more than hundreds of bands), many class outputs, high correlation between bands, and very limited availability of reference data [1]. So the traditional classification methods [2] cannot give correct classification result for the case of hyper spectral image. Therefore, powerful techniques are required in order to improve classification accuracy. Different researchers attempted the HIS classification using variety of approaches and techniques are derived. A typical flow of work includes major steps as noise removal, feature extraction and classification. In this paper we tried to improve classification accuracy by adopting new methods at noise removal and feature selection stages. The arrangement of paper is follows as. We exhibit available literature survey in Sec- II. Methodology is presented in Sec-III. Methodology and datasets description is in Sec-IV. In conclusion, we finish up with Sec-V where we talk about real open issues and future work. 2. Literature Survey Different researchers attempted and formulated solution to the problem of HSI classification in the last 2-3 decades [11-13]. Several robust algorithms with some extensions are designed. HSI classification research can be divided into supervised, unsupervised, semisupervised and other methods like active and transfer learning. Among these the most promising methods can be roughly divided into 3 types. SVM based methods, SRC based methods and MLR based methods. In brief, the proper utilization of spatial information will mostly affect the performance of HSI classification. Even though they are several approaches shown the significant performances still they are some lapses [14] [15]. Xiangrong Zhang, Qiang Song [7], have proposed new method of sample selection based on modified cotraining model for HSI classification. Two views are constructed by using both spatial and spectral information. 2D Gabor wavelet is used to extract spatial features and original spectral features are used to form another view. The accuracy of algorithm increases with more number of iterations which makes it expensive in terms of time cost [5]. 121

2 Wei Zhu et al [8] have proposed a graph based nonlocal total variation for classification. For handling the variation the authors have proposed the primal-dual hybrid gradient algorithm. Sen Jia et al [9] have proposed a super pixel based learning algorithm for classification. Also used multi task learning algorithm is implemented. The authors have attempted to address the sample size issues in the labeling. Initially, a hyper spectral image is segmented into different homogenous parts called super pixels. Features are extracted from the image using 2D Gabor Filter which formulates a Gabor Cube and a super pixel map is generated simultaneously. Then spatial spectral Schrodinger Eigen maps are used to reduce the dimension followed by Support Vector Machine for classification. The data sets are land cover classes with the number of for the Indian Pines, the Pavia and Salina Data Sets. Yanni Dong et al [10] proposed ensemble learning algorithm for dimensionality reduction followed by support vector machine is implemented [6]. The authors [3] has designed robust algorithm with series of steps in solving classification of HSI. This has shown significant results as compared with state of art methods. However we have contributed very minor aspects as Inclusion of noise removal before the application of 3D DWT and Selection of the training pixels using stratified sampling method. 3. Methodology The Proposed flow of work for HIS classification is shown in Figure-1. HIS cube is the input for the process. First step is to conduct noise removal. Immediate step is to perform feature extraction. This can be done using 3 dimensional discrete wavelet transform. Wavelet transform is a powerful tool which is useful in time frequency analysis. In the work of [3] 3D discrete wavelet transform is applied for HIS Classification where 3D-DWT is equal to three 1D DWT s. Further the process includes sample selection using Stratified Sampling and classification using probabilistic SVM. Figure 1. Hyper spectral Image Classification Procedure Mathematics of Stratified Sampling Let C denotes class of HIS. For example 16 classes of Indian pines data set are represented by C i i: 1 to 16 Let sample size selected be S Number of pixels in each C i denoted by P i If X is Population size Then the number of training pixels that can be selected from each C i among P i is T i T i =S/X* P i (1) In brief entire process (SVM-3DG with Stratified Random sampling) can be shown in the form of algorithm for is given below: Algorithm: SVM-3DDWT with Stratified Random sampling hyper spectral image classification Input: Hyper spectral image data X R HxWxB smoothness parameter ß, the number of classes K. Output: Labels y^ 1. Convert X in to Z^ using 3D-DWT, and convert in to a matrix 2. Apply noise removal 3. Select in Z as training using Stratified sampling and use the remaining as testing ones 4. Train the probabilistic SVM classifier using training 5. Compute the classification labels y 6. Obtain classification map 4. Experimental results and Datasets used In order to execute the proposed method and validate the obtained results we have used two HIS data sets namely Indian Pines and University of Pavia. The complete description of the data sets is shown below. Indian Pines data was gathered by AVIRIS sensor over the Indian Pines test site in North-western Indiana and consists of 145\times145 pixels and 224 spectral reflectance bands in the wavelength range meters. This scene is a subset of a larger one. The Indian Pines scene contains two-thirds agriculture, and one-third forest or other natural perennial vegetation. There are two major dual lane highways, a rail line, as well as some low density housing, other built structures, and smaller roads. Since the scene is taken in June some of the crops present, corn, soybeans, are in early stages of growth with less than 5% coverage. The ground truth available is designated into sixteen classes and is not all mutually exclusive. We have also reduced the number of bands to 200 by removing bands covering the region of water absorption: [ ], [ ], 220. University of Pavia data set has two scenes acquired by the ROSIS sensor during a flight campaign over Pavia, northern Italy. It consists of 102 number of spectral bands for Pavia Centre and 103 for Pavia 122

3 University. Pavia Centre is a 1096*1096 pixels image, and Pavia University is 610*610 pixels, but some of the in both images contain no information and have to be removed before the analysis. The geometric resolution is 1.3 meters. Both image ground truths differentiate 9 classes each. The results obtained after implementation of proposed method on Indian Pines data set are shown in Figure 2. (a) (b) (c) (d) (e) (f) Figure 2. Classification results on Indian Pines dataset (a)indian Pines HSI (b) Ground truth Map (c) Training Map(d) Test Map (e) Classification result (f) Segmentation result. In order to validate the results we have used three parameters. First Overall accuracy (OA) means Percentage of pixels correctly labeled, in classification. Second one is Average accuracy (AA) which is mean Percentage of correctly labeled pixels for each class. Third parameter is Kappa coefficient (KC) which describes Percentage of correctly classified pixels corrected by no of agreements that would be expected purely by chance. Table 2. Over all accuracy, Average accuracy and Kappa and SVM (Indian Pines dataset) Process Over all accuracy (OA) Average accuracy (AA) Classification Segmentation Kappa coefficient (KC) Over all accuracy, Average accuracy and Kappa and SVM are tabulated in Table2. Table 3. Over all accuracy, Average accuracy and Kappa and SVM with stratified sampling (Indian Pines dataset). Process Overall accuracy(oa) Average accuracy (AA) Kappa coefficient (KC) Classification Segmentation Also the Overall accuracy, Average accuracy and Kappa coefficient values for HIS classification using 3D- DWT and SVM with stratified sampling for Indian Pines dataset are tabulated in Table3. For University of Pavia dataset are tabulated in Table4. 123

4 Table 4. Over all accuracy, Average accuracy and Kappa and SVM with stratified sampling (University of Pavia dataset). Process Overall accuracy(oa) Average accuracy(aa) Kappa coefficient (KC) Classification Segmentation In the process of above obtained results, Stratified sampling is adopted to select train. Here the whole population is divided into different clusters (homogeneous) and are selected from each cluster (using random sampling or systematic sampling). Initially, sample size should be fixed. In this case, it is better to use systematic sampling as it reduces the sampling error. Suppose the sample size is 370. Here, the population size is 10,249. Calculation of number of from each cluster using stratified sampling is shown in Table5. Statistics of the Indian Pines data set after incorporating stratified sampling, including the name, number of training, test and total for each class are shown in Table6. Table 5. Calculation of number of from each cluster using stratified sampling Cluster number Cluster size Calculation of weight Number of Table 6. Statistics of the Indian Pines data set, including the name, the number of training, test and total for each class using stratified sampling S.No Name of the Sample Train Test Total 1 Alfalfa Corn-no till Corn-min til Corn Grass-pasture Grass-trees Grass- 27 pasture- mowed Haywindrowed Oat Soybean-no 937 till Soybean-min 2366 till Soybeanclean Wheat Woods Buildings- 372 Grass-Trees- Drives Stone-Steel- 90 Towers Conclusion In this paper our contribution is experimented to the 3D DWT & SVM based HSI classification. The key aspect is to use the stratified sampling to select number of drawing pixels and thus test pixels. Experimental results on two bench mark datasets Indian pines and Pavia shows that very slight improvement in terms of OA, AA and kappa coefficient values. Future work can be done by improving accuracy by using other statistical parameters and test across all the evaluable datasets. References [1] Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, Multiple spectral-spatial classification approach for hyperspectral data, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp , Nov [2] F. Melgani and L. Bruzzone, Classification of hyperspectral remote sensing images with support vector 124

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