Browsing by Author "Vasuki, S."

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Clustering based band selection for endmember extraction using simplex growing algorithm in hyperspectral images
(Springer, 2017-03) Veera SenthilKumar, G.; Vasuki, S.
With the advancement in technology, hyperspectral images have potential applications in the field of remote sensing due to their high spectral resolution. Despite the hyperspectral image providing abundant information, its analysis suffers from the problem of high dimensionality. Hence, Dimensionality Reduction (DR) is an essential task in all hyperspectral image analysis. Band Selection, which is one of the DR techniques, is still a challenging issue even though many algorithms have been developed. To provide remedy for this issue, this paper explores a novel approach for band selection using K-means clustering on statistical feature in hyperspectral images. The proposed method of clustering based band selection for DR is simple and accurate. A reliable estimate of number of bands to be selected is provided by Virtual Dimensionality (VD). Informative bands preserving maximum information are selected based on the statistical feature, the variance using K-means Clustering technique. Further, our proposed work involves the utilization of the effectiveness of Simplex Growing Algorithm (SGA) on endmember extraction in association with clustering based band selection. Using Fully Constrained Least Squares (FCLS) method, abundance fraction is estimated based on endmember signatures, which are derived using Endmember Extraction Algorithm (EEA). The proposed work is investigated and compared with that of N-FINDR and Vertex Component Analysis (VCA) algorithms. The performance of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), Spectral Angle Distance (SAD) and computation time. Experimental results show that the proposed clustering based band selection with SGA endmember extraction algorithm reduces the average SAD by 8 to 10 % and the average RMSE by nearly 1 %, compared to that of N-FINDR and VCA algorithms. In terms of computation time, the proposed band selection based DR with SGA algorithm is seven times faster than conventional transform based DR with SGA algorithm
Maximin distance based band selection for endmember extraction in hyperspectral images using simplex growing algorithm
(Springer, 2017-03) Veera SenthilKumar, G.; Vasuki, S.
With the fast growing technologies in the field of remote sensing, hyperspectral image analysis has made a great breakthrough. It provides accurate and detailed information of objects in the image when compared to any other remotely sensed data. It is possible because of its high redundancy in nature. But this redundancy in hyperspectral images leads to high computational complexity in their analysis. Hence Dimensionality Reduction (DR) is a significant task in all hyperspectral image processing. DR can be achieved either by feature extraction or feature selection. Feature selection or Band selection is adopted in this paper because of no compromise in original data. Despite many algorithms that exist for band selection, this paper proposes a new concept of Maximin distance algorithm using Spectral Angle Distance (SAD) as distance measure for band selection. Virtual Dimensionality (VD) is used to provide the number of bands to be selected because it has been proved to be reliable estimate. Simplex Growing Algorithm (SGA) is deployed for endmember extraction in the experiment work. In order to evaluate the performance of the proposed band selection algorithm, the Spectral Angle Distance (SAD) and Spectral Similarity Value (SSV) are used as measures. The efficacy of our proposed algorithm has been proved from experimental results in comparison with Constrained Band Selection (CBS), Similarity Based Band Selection (SBBS), Clustering Based Band Selection (CBBS), Uniform Band Selection (UBS), Minimum Variance Principal Component Analysis (MVPCA) and Exemplar Component Analysis (ECA) and Firefly Algorithm Based Band Selection (FABBS).
Segmentation of color images using EM Cost with spatial refinement algorithm on MBWT Features
(IJCSET, 2011-03) Vasuki, S.; Veera SenthilKumar, G.; Ganesan, L.
This paper proposes a novel technique to segment the color images combining M-Band Wavelet transform(MBWT) and Expectation Maximization (EM) with cost spatial refinement algorithm. One of the drawbacks of standard wavelets is that they are not suitable for the analysis of high frequency signals with relatively narrow bandwidth. This drawback has been overcome using MBWT. Also M-band wavelet decomposition yields a large number of sub bands which is required for improving the performance accuracy. The proposed algorithm first decomposes the input image into sixteen subimages by applying MBWT. Then, median feature is computed for each subimage and maximum energy subimage is chosen as the appropriate feature space on which EM with cost spatial refinement algorithm is applied. This new combined algorithm produces very good segmentation results by taking advantage of M-Band Wavelet feature extraction and EM with cost spatial refinement algorithm. The segmentation result is more homogeneous and quite consistent with the visualized color distribution in the objects of the original images compared to Fuzzy C means and K means spatial refinement algorithms. Also EM with cost spatial refinement algorithm needs less computational time compared to other clustering algorithms.