PolSAR terrain classification based on image segmentation and EM algorithm

doi:10.7523/j.ucas.2021.0011

Abstract

Abstract: In the study of the terrain classification based on the polarimetric synthetic aperture radar (PolSAR), the image segmentation algorithm based on deep neural network has the disadvantages of fuzzy classification boundary, low classification accuracy and complicated calculation caused by the redundancy of high-dimensional feature information. This paper proposes a lightweight segmentation network based on convolutional neural network and EM algorithm called low-rank-reconstruction-net (LRR-Net), which is applied to the terrain classification of fully PolSAR images. Starting from the idea of polarimetric target decomposition, LRR-Net uses the EM algorithm to perform low-rank reconstruction of features, maps the features from high-dimensional space to low-dimensional space, achieving higher classification accuracy while reducing parameters. The model is trained and evaluated in GF-3 fully PolSAR dataset, and the results show that the model complexity is reduced under the guarantee of the classification accuracy.

Key words: PolSAR, terrain classification, neural network, low-rank reconstruction

CLC Number:

TN958

CAO Zhe, FENG Shanshan, SUN Xian, HONG Wen. PolSAR terrain classification based on image segmentation and EM algorithm[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(5): 639-647.

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