Land Cover Classification of Fully Polarimetric SAR with Encoder-Decoder Network and Conditional Random Field
ZHAO Quanhua1, XIE Kailang1, WANG Guanghui2, LI Yu1
1.Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University, Fuxin 123000; 2.Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of the People's Republic of China, Beijing 100048
Abstract：Aiming at weak characterization of polarimetric synthesis aperture radar(PolSAR) image feature classification and low classification accuracy of the traditional fully convolutional network(FCN), a land cover classification algorithm of PolSAR with encoder-decoder network(E-D-Net) and conditional random field(CRF) is proposed. Firstly, Freeman decomposition and Pauli decomposition are employed to model PolSAR images and extract scattering features corresponding to each decomposition. Symmetric network model is built via semantic segmentation network model and multi-scale convolution unit. An E-D-Net network model is designed by embedding the multi-scale asymmetric convolution unit into the middle layer. PolSAR image and scattering features of Freeman decomposition are autonomously learned by E-D-Net to obtain the initial classification result. Finally, the CRF combined with Pauli coherent decompositionfalsecolorimageinformation isutilized todenoiseandsmooth the initialclassification results to obtain the final classification result. PolSAR image experiments on two areas verify the effectiveness and the feasibility of the proposed algorithm.
赵泉华, 谢凯浪, 王光辉, 李玉. 结合编码-解码网络和条件随机场的全极化合成孔径雷达土地覆盖分类[J]. 模式识别与人工智能, 2019, 32(12): 1122-1132.
ZHAO Quanhua, XIE Kailang, WANG Guanghui, LI Yu. Land Cover Classification of Fully Polarimetric SAR with Encoder-Decoder Network and Conditional Random Field. , 2019, 32(12): 1122-1132.
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2019, Vol. 32 
