Classification of SAR Images Based on Entropy

Full Text (PDF, 334KB), PP.82-86

Views: 0 Downloads: 0

Author(s)

Debabrata Samanta 1,* Goutam Sanyal 1

1. Department of CSE, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, West Bengal, 713209, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2012.12.09

Received: 20 Jan. 2012 / Revised: 13 May 2012 / Accepted: 7 Aug. 2012 / Published: 8 Nov. 2012

Index Terms

Entropy, SAR Image, Multi-Nominal Region, Grouped–Entropy value

Abstract

SAR image classification is the progression of separating or grouping an image into different parts. The good feat of recognition algorithms based on the quality of classified image. The good recital of recognition algorithms depend on the quality of classified image. The proposed classification method is hierarchical: classes which are difficult to distinguish are grouped.An important problem in SAR image application is accurate classification. In this paper, we developed a new methodology of SAR image Classification by Entropy. The severance between different groups or classes is based on logistic and multi-nominal regression, which finds the best combination of features to make the separation and at the same time perform a feature selection depending on Grouped –Entropy value.

Cite This Paper

Debabrata Samanta, Goutam Sanyal, "Classification of SAR Images Based on Entropy", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.12, pp.82-86, 2012. DOI:10.5815/ijitcs.2012.12.09

Reference

[1]Anthony Tung Shuen Ho, Eliza Steve Seumahu, Siu Chung Tam, Teck Wei Chin, Hock Seng Lim , “Improving SAR Image classification In Tropical Region Through Fusion With SPOT Data”. pp 1596-1598. 0-7803-4403-0/98/$10.00 0 1998 IEEE.

[2]Mar´ıa Elena Buemi, Marta Mejail, Julio Jacobo and Juliana Gambini, “Improvement in SAR Image Classification using Adaptive Stack Filters”.

[3]Gabriele Moser, Vladimir Krylov, Sebastiano B. Serpico, Josiane Zerubia , “High resolution SAR-image classification by Markov random fields and finite mixtures” , IS&T/SPIE Electronic Imaging 2010.

[4]Lorenzo Bruzzone, Mattia Marconcini, Urs Wegmüller, Andreas Wiesmann , “An Advanced System for the Automatic Classification of Multitemporal SAR Images” , IEEE transactions on geosciences and remote sensing, VOL. 42, NO. 6, JUNE 2004.

[5]Lalit Gupta, Shivani G. Rao, Sukhendu Das, “classification of textures in sar images using multi-channel multi-resolution filters”.

[6]Yeong-Sun Song, Hong-Gyoo Sohn, and Choung-Hwan Park , “Efficient Water Area Classification Using Radarsat-1 SAR Imagery in a High Relief Mountainous Environment” , Photogrammetric Engineering & Remote Sensing ,Vol. 73, No. 3, March 2007, pp. 285–296.

[7]SUN Xiao-xia, ZHANG Ji-xian, LIU Zheng-jun and ZHAO zheng, “Classification from airborne SAR data enhanced by optical image using an object-oriented approach”.

[8]Roger Fjørtoft, Franck S´ery Danielle Ducrot , Armand Lop`es, Cedric Lemar´echal, Christelle Fortier, Philippe Marthon, Eliane Cubero-Castan , “Segmentation, filtering and classification of SAR images”, LIMA/ENSEEIHT and CESBIO under contracts 833/CNES/94/1022/00 and 833/CNES/96/0574/00.

[9]Uma S. Ranjan, Akash Narayana , “Classification of objects in SAR images using scaling features”.

[10]Dirk Borghys, Yann Yvinec, Christiaan Perneel, Aleksandra Pizurica and Wilfried Philips, “Hierarchical Supervised Classification of Multi-Channel SAR Images”, pp. 1-5.

[11]L. Bruzzone, F. Roli, and S. B. Serpico, “Structured neural networks for signal classification,” Signal Process. vol. 64, no. 3, pp. 271–290, Feb.1998.

[12]S. B. Serpico, L. Bruzzone, and F. Roli, “An experimental comparison of neural and statistical nonparametric algorithms for supervised classification of remote-sensing images,” Pattern Recognit. Lett., vol. 17, no. 13, pp. 1331–1341, 1996.

[13]S. Quegan, T. Le Toan, J. J. Yu, F. Ribbes, and N. Floury, “Multitemporal ERS SAR analysis applied to forest mapping,” IEEE Trans. Remote Sensing, vol. 38, pp. 741–753, Mar. 2000.

[14]U.Wegmüller, A.Wiesmann, T. Strozzi, and C. L.Werner, “Forest mapping with multitemporal SAR,” in Proc. ForestSAT’02 Conf., Edinburgh, U.K., Aug. 5–9, 2002.

[15]T. Le Toan, S. Quegan, M. Davidson, and J. M. Martinez, “Radar remote sensing of forests. Applications using existing satellite SAR data,” in Proc. 8th Int. Symp. Physical Measurements Signatures Remote Sensing, 2001, pp. 33–40.

[16]Krylov, V. and Zerubia, J., High resolution SAR image classi_cation," Research Report 7108, INRIA (2009).

[17]Besag, J., On the statistical analysis of dirty pictures," Journal of the Royal Statistical Society B 48, 259{302 (1986).

[18]Geman, S. and Geman, D., Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images," IEEE Trans. Patt. Anal. Mach. Intell. 6, 721{741 (1984).

[19]Yu, Y. and Cheng, Q., MRF parameter estimation by an accelerated method," Pattern Recognit. Lett. 24(9-10), 1251{1259 (2003).

[20]Kato, Z., Zerubia, J., and Berthod, M., Satellite image classi_cation using a modi_ed Metropolis dynam-ics," in [Proceedings of ICASSP], 573{576 (1992).