Abstract
Classification approach helps to extract the important information from satellite images, but it is quite effective on extracting the information from mixed classes. The problem of classes is well known in the area of satellite image processing due to similar spectral resolution among the few classes (objects). It is quite obvious in multispectral data which is having little variation in spectral resolution with heterogeneous classes. In earlier times, Neural network based classification has been used widely, but at a cost of high time and computation complexity. To resolve the problem of the mixed classes due to the spectral behavior in sufficient time, a novel method Equivariant Robust Independent Component Analysis (ERICA) is proposed. This algorithm separates the objects from mixed classes, which shows similar spectral behavior. It can easily predict the objects without using of pre-whitening technique. Therefore, pre-whitening is not playing an important role in convergence of the algorithm. Due to Quasi-Newton based iteration in this algorithm helps to converge to a saddle point with locally isotropic convergence, regardless of the spatial and spectral distributions of satellite images. Hence, this proposed ERICA gives major contribution for classification of satellite images in healthy trees, buildings and road areas. Another important one in the image is shadow information, which helps to show elevated factor due to high rising buildings and flyovers in emerging suburban areas. The experimental results of the remote sensing data clearly indicates that the proposed ERICA has better classification accuracy and convergence speed, and is also appropriate to solve the image classification problems.
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References
Almeida, L.B., Silva, F.M.: Adaptive Decorrelation. In: Aleksander, I., Taylor, J. (eds.) Artificial Neural Networks, vol. 2, pp. 149–156. Elsevier Science Publishers (1992)
Amari, S.: Natural gradient work efficiently in learning. Neural Comput. 10(2), 251–276 (1998)
Benediktsson, J.A., Pesaresi, M., Arnason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing 41(9), 1940–1949 (2003)
Cardoso, J.F., Laheld, B.: Equivariant adaptive source separation. IEEE Trans. Signal Process. 44(12), 3017–3030 (1996)
Choi, S., Cichocki, A., Amari, S.: Flexible independent component analysis. Journal of VLSI Signal Processing 26(1/2), 25–38 (2000)
Cichocki, A., Unbehauen, R., Rummert, E.: Robust learning algorithm for blind separation of signals. Electronics Letters 30(17), 1386–1387 (1994)
Cichocki, A., Georgiev, P.: Blind Source Separation Algorithms with Matrix Constraints. IEICE Trans. on information and Systems, Special Section on Special issue on Blind Signal Processing E86-A(1), 522–531 (2003)
Cruces, S., Castedo, L., Cichocki, A.: Novel Blind Source Separation Algorithms Using Cumulants. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, V, Istanbul, Turkey, pp. 3152–3155 (2000)
Kelley, C.T.: Iterative methods for linear and nonlinear equations. In: Frontiers in Applied Mathematics, vol. 16, pp. 71–78. SIAM, Philadelphia (1995)
Li, G., Wan, Y., Chen, C.: Automatic building extraction based on region growing, mutual information match and snake model. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds.) ICICA 2010. CCIS, vol. 106, pp. 476–483. Springer, Heidelberg (2010)
Segl, K., Kaufmann, H.: Detection of small objects from high-resolution panchromatic satellite imagery based on supervised image segmentation. IEEE Transactions on Geoscience and Remote Sensing 39(9), 2080–2083 (2001)
Singh, P.P., Garg, R.D.: A Hybrid approach for Information Extraction from High Resolution Satellite Imagery. International Journal of Image and Graphics 13(2), 340007(1-16) (2013)
Yang, J.H., Liu, J., Zhong, J.C.: Anisotropic diffusion with morphological reconstruction and automatic seeded region growing for colour image segmentation. In: Yu, F. (ed.) Proceedings of the International Symposium on Information Science and Engineering, Shangai, China, pp. 591–595 (2008)
Zhang, L., Amari, S., Cichocki, A.: Natural Gradient Approach to Blind Separation of Over- and Under-complete Mixtures. In: Proc. of Independent Component Analysis and Signal Separation, Aussois, France, pp. 455–460 (1999)
Zhang, L., Amari, S., Cichocki, A.: Equi-convergence Algorithm for blind separation of sources with arbitrary distributions. In: Mira, J., Prieto, A.G. (eds.) IWANN 2001. LNCS, vol. 2085, pp. 826–833. Springer, Heidelberg (2001)
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Singh, P.P., Garg, R.D. (2014). Classification of High Resolution Satellite Images Using Equivariant Robust Independent Component Analysis. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_34
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DOI: https://doi.org/10.1007/978-3-319-07353-8_34
Publisher Name: Springer, Cham
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