Open AccessComplex-valued U-Net for PolSAR Image Semantic Segmentation
Author(s) -
Ao Liu,
Li Yu,
Zhaoxin Zeng,
Xiaochun Xie,
Yuting Guo,
Qiqi Shao
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012102
Subject(s) - overfitting , segmentation , net (polyhedron) , computer science , artificial intelligence , pattern recognition (psychology) , image segmentation , image (mathematics) , scale space segmentation , segmentation based object categorization , mathematics , artificial neural network , geometry
As an image semantic segmentation network, U-Net has the advantage of simple structure which is suitable for semantic segmentation of PolSAR images with small datasets. However, the original U-Net is a real-valued (RV) network, whose input must be RV. If it is directly used in the segmentation of PolSAR image, the complex-valued (CV) input must be converted into RV, which results in the loss of information. In this paper, a CV U-Net, which is mathematically strict, is proposed for semantic segmentation of PolSAR images. Considering that the PolSAR dataset is small, the structure and parameters of CV U-Net are furtherly simplified based on the original U-Net to prevent overfitting. Experimental results on the Flevoland dataset show that the proposed CV U-Net has better segmentation performance than the original RV U-Net and some other semantic segmentation networks.