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PolSAR image classification based on complex-valued convolutional neural network and Markov random field
Author(s): Xianxiang Qin; Wangsheng Yu; Peng Wang; Tianping Chen; Huanxin Zou
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Paper Abstract

Recently, a complex-valued convolutional neural network (CV-CNN) has been used for the classification of polarimetric synthetic aperture radar (PolSAR) images, and has shown superior performance to most traditional algorithms. However, it usually yields unreliable results for the pixels distributing within heterogeneous regions or the edge areas. To solve this problem, in this paper, an edge reassigning scheme based on Markov random field (MRF) is considered to combine with the CV-CNN. In this scheme,both the polarimetric statistical property and label context information are employed. The experiments performed on a benchmark PolSAR image of Flevoland has demonstrated the superior performance of the proposed algorithm.

Paper Details

Date Published: 31 July 2019
PDF: 7 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980B (31 July 2019); doi: 10.1117/12.2540913
Show Author Affiliations
Xianxiang Qin, Air Force Engineering Univ. (China)
Wangsheng Yu, Air Force Engineering Univ. (China)
Peng Wang, Air Force Engineering Univ. (China)
Tianping Chen, Air Force Engineering Univ. (China)
Huanxin Zou, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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