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Classification of patterns on high resolution SAR images

Publication Type : Conference Paper

Publisher : 2015 International Conference on Computing and Network Communications, CoCoNet 2015

Source : 2015 International Conference on Computing and Network Communications, CoCoNet 2015, Institute of Electrical and Electronics Engineers Inc., p.784-792 (2015)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-84964900429&partnerID=40&md5=609aaaeafb25b1fd69f4598757041df4

ISBN : 9781467373098

Keywords : Color, Feature extracting method, High-resolution SAR, IKONOS satellite, image classification, Image denoising, Local binary patterns, Maximum likelihood, Maximum likelihood estimation, Military applications, Neural networks, Non- local means filters, Pattern matching, Radar, Radar imaging, Remote sensing, Remote sensing applications, RGB color space, SAR image classifications, Space-based radar, Synthetic aperture radar

Campus : Amritapuri, Coimbatore

School : Department of Computer Science and Engineering, School of Engineering

Department : Computer Science

Year : 2015

Abstract : Synthetic Aperture Radar being an all weather adaptive and deeply penetrating, forms an inevitable part of all processes of investigation. Classifying different patterns like rivers, buildings, land areas, farm land etc has got prominent role in remote sensing applications, military applications etc and hence has been actively researched in recent years. This paper presents a novel approach for classifying high resolution SAR images. Image denoising is the first step in certain applications like classification problem, pattern matching etc. Here a modified Non Local Means filter method is used for denoising and also explores the possibility of using Artificial Neural Networks (ANN) for classifying different patterns on high resolution SAR images based on a fusion method. The proposed method uses the features of Local Binary Patterns (LBP), features in RGB color space and features in HSV color space. The experiments on high resolution SAR images obtained from Quickbird and Ikonos satellites shows that the proposed method outperforms the other widely used feature extracting methods in SAR image classification. © 2015 IEEE.

Cite this Research Publication : B. Bhadran and Jyothisha J. Nair, “Classification of patterns on high resolution SAR images”, in 2015 International Conference on Computing and Network Communications, CoCoNet 2015, 2015, pp. 784-792

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