PolSAR image classification based on complex-valued convolutional neural network and Markov random field

Xianxiang Qin; Wangsheng Yu; Peng Wang; Tianping Chen; Huanxin Zou

doi:10.1117/12.2540913

31 July 2019 PolSAR image classification based on complex-valued convolutional neural network and Markov random field

Xianxiang Qin, Wangsheng Yu, Peng Wang, Tianping Chen, Huanxin Zou

Proceedings Volume 11198, Fourth International Workshop on Pattern Recognition; 111980B (2019) https://doi.org/10.1117/12.2540913
Event: Fourth International Workshop on Pattern Recognition, 2019, Nanjing, China

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.

Citation Download Citation

Xianxiang Qin, Wangsheng Yu, Peng Wang, Tianping Chen, and Huanxin Zou "PolSAR image classification based on complex-valued convolutional neural network and Markov random field", Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980B (31 July 2019); https://doi.org/10.1117/12.2540913

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KEYWORDS

Image classification

Polarimetry

Convolutional neural networks

Magnetorheological finishing

Synthetic aperture radar

Buildings

Algorithm development

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