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Proceedings Paper

Texture recognition of SAR image based on surfacelet transform
Author(s): Dandan Liu; Can Zhao; Chunrui Tang
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Paper Abstract

In order to take full use of SAR image dark areas information, texture recognition method of SAR image based on Surfacelet transform is proposed. Firstly, SAR image in test samples is decomposed into brightness SAR image and reflection SAR image based on Retinex model; secondly, the decomposed SAR image carries through Surfacelet transform, in order to reduce computational complexity and shorten the follow-up recognition time, the decomposed SAR image is processed two dimension three level scale Surfacelet transform, and the first level is 8 directions, the others are 8,2 directions respectively, after image Surfacelet transform, the sparsity of the distribution coefficient is even more evident, especially high-frequency sub-band; thirdly, coefficient matrixes of high-frequency sub-band reduce dimension processing by using the singular value decomposition theory of matrix; fourthly, the final eigenvector are composed of character of energy of coefficient matrix in low-frequency sub-band and first and second-order moment of coefficient matrix in intermediate frequency sub-band and eigenvector by reducing dimension in high-frequency subband; at last, use BP neural network to train with supervised and recognize of samples. The method takes full use of SAR image dark areas information, and the simulation results show that the method the recognition rate is better than other methods.

Paper Details

Date Published: 22 October 2010
PDF: 5 pages
Proc. SPIE 7658, 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology, 76583R (22 October 2010); doi: 10.1117/12.866701
Show Author Affiliations
Dandan Liu, Heilongjiang Institute of Science and Technology (China)
Can Zhao, Heilongjiang Institute of Science and Technology (China)
Chunrui Tang, Heilongjiang Institute of Science and Technology (China)


Published in SPIE Proceedings Vol. 7658:
5th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Detector, Imager, Display, and Energy Conversion Technology
Yadong Jiang; Bernard Kippelen; Junsheng Yu, Editor(s)

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