Abstract
This paper presents a novel method for segmentation and change detection of multispectral images using proximal splitting-based clustering and multiclass support vector machine (MSVM). Initially, the multitemporal satellite images are preprocessed and then textures are extracted using Difference of Offset Gaussian filter. In general, the traditional clustering method uses Euclidean distance as a prime factor for segmentation process. For multitextured images such as remotely sensed images, this metric provides inconsistent output. To achieve better segmented results, proximal splitting algorithm has been proposed. This method has been considered as a solution for iterative minimization problem, which is required to find exact changes between the multitemporal images. The MSVM is chosen to group the segmented clusters into a fixed number of classes, since the clusters obtained from the proximal splitting algorithm are not independent with each other. Then, the classified images are subjected to image differencing method to detect the changes. Experimentation is performed with two real data sets of Landsat7 images, which illustrates that the mean of difference in area obtained by the proposed method is reduced by an average of 35.24% compared to the conventional system. The validity index obtained for data set 1 using proposed algorithm is lower than the existing methods.
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Bandyopadhyay, S., & Maulik, U. (2002). An evolutionary technique based on K-means algorithm for optimal clustering in RN. Information Sciences, 146(1), 221–237.
Bárdossy, A., & Samaniego, L. (2001). Fuzzy rule-based classification of remotely sensed imagery. IEEE Transactions on Geoscience Remote Sensing, 40(2), 362–374.
Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences, 10(2–3), 191–203.
Bovolo, F., & Bruzzone, L. (2005). A detail-preserving scale-driven approach to change detection in multitemporal SAR images. IEEE Transactions on Geoscience and Remote Sensing, 43(12), 2963–2972.
Bruzzone, L., & Prieto, D. F. (2000). Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1171–1182.
Cao, G., Zhou, L., & Li, Y. (2016). A new change-detection method in high-resolution remote sensing images based on a conditional random field model. International Journal of Remote Sensing, 37, 1173–1189.
Celik, T. (2009). Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geoscience and Remote Sensing Letters, 6(4), 772–776.
Celik, T., & Ma, K.-K. (2010). Unsupervised change detection for satellite images using dual-tree complex wavelet transform. IEEE Transactions on Geoscience and Remote Sensing, 48(3), 1199–1210.
Chang, T., & Jay Kuo, C. C. (1993). Texture analysis and classification with tree-structured wavelet transform. IEEE Transactions on Image Processing, 2(4), 429–440.
Chen, Y., & Cao, Z. (2013). Change detection of multispectral remotesensing images using stationary wavelet transforms and integrated active contours. International Journal of Remote Sensing, 34(24), 8817–8837.
Chen, Y.-K., & Wu, X. (2001). CT image segmentation based on clustering and graph cuts. Advanced in Control Engineering and Information Science Procedia Engineering, 15, 5179–5184.
Christophe, R. M., & Bisho, P. (1995). Neural networks for pattern recognition. Birmingham, Oxford: Aston University, Clarendo Npress.
Coppin, P., et al. (2004). Review article digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596.
Del Frate, F. (2007). Use of neural networks for automatic classification from high-resolution images. IEEE Transactions on Geoscience and Remote sensing, 43(4), 800–809.
Felzenszwalb, P. F., & Huttenlocher, D. P. (2004). Efficient graph-based image segmentation. International Journal of Computer Vision, 59(2), 167–181.
Femando, W. S. P., Lanka, U., & Prbudu, P. (2007). Identification of moving obstacles with pyramidal Lucas Kanade optical flow and K means clustering. In IEEE international conference on information and automation for sustainability (pp. 111–117). Washington, DC: IEEE.
Gandhimathi Alias Usha, S., & Vasuki, S. (2018). Improved segmentation and change detection of multi-spectral satellite imagery using graph cut based clustering and multiclass SVM. Multimedia Tools and Applications, 77(12), 15353–15383.
Kasetkasem, T., & Varshney, P. (2002). An image change detection algorithm based on Markov random field models. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1815–1823.
Kusetogullari, H., Yavariabdi, A., & Celik, T. (2015). Unsupervised change detection in multitemporal multispectral satellite images using parallel particle swarm optimization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(5), 2151–2164.
Li, H., Gong, M., & Liu, J. (2015). A local statistical fuzzy active contour model for change detection. IEEE Geoscience and Remote Sensing Letters, 12(3), 582–586.
Liu, S. Q., Du, Q., et al. (2017). Multiscale morphological compressed change vector analysis for unsupervised multiple change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4124–4137.
Long, T. N., et al. (2015). Semi-supervising interval type-2 fuzzy C-means clustering with spatial information for multi-spectral satellite image classification and change detection. Computers and Geosciences, 83, 1–16.
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of remote sensing, 28(5), 823–870.
Pal, N. R., & Pal, S. K. (1993). A review on image segmentation techniques. Pattern Recognition, 26(9), 1277–1294.
Raja, R. A. (2013). Wavelet based post classification change detection technique for urban growth monitoring. International Journal of Indian Society of Remote Sensing, 41(1), 35–43.
Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of remote sensing, 10(6), 989–1003.
Srivastava, P. K., et al. (2012). Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50(9), 1250–1265.
Stavrakoudis, D. G., & Galidaki, G. N. (2012). A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery. IEEE Transactions on Geoscience and Remote sensing, 50(1), 130–148.
Volpi, M. (2012). Unsupervised change detection with kernels. IEEE Transactions on Geoscience and Remote Sensing, 9, 6.
Volpi, M., Tuia, D., Camps-Valls, G., & Kanevski, M. (2010). Unsupervised change detection by kernel clustering. In Proceedings of SPIE, image and signal processing for remote sensing (Vol. 7830:783 00V-1–783 00V-8).
Wang, W., & Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets Systems, 158, 2095–2117.
Yavariabdi, A., & Kusetogullari, H. (2017). Change detection in multispectral landsat images using multiobjective evolutionary algorithm. IEEE Geoscience and Remote Sensing Letters, 14(3), 414–418.
Zhang, Y., Peng, D., & Huang, X. (2018). Object-based change detection for VHR images based on multiscale uncertainty analysis. IEEE Geoscience and Remote Sensing Letters, 15, 13–17.
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Gandhimathi Alias Usha, S., Vasuki, S. A Novel Method for Segmentation and Change Detection of Satellite Images Using Proximal Splitting Algorithm and Multiclass SVM. J Indian Soc Remote Sens 47, 853–865 (2019). https://doi.org/10.1007/s12524-019-00941-7
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DOI: https://doi.org/10.1007/s12524-019-00941-7