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Keywords = Lovász-softmax loss optimization SAR net (LoSARNet)

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18 pages, 6454 KB  
Article
Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization
by Rui Guo, Xiaopeng Zhao, Guanzhong Zuo, Ying Wang and Yi Liang
Remote Sens. 2023, 15(19), 4802; https://doi.org/10.3390/rs15194802 - 1 Oct 2023
Cited by 4 | Viewed by 4105
Abstract
The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is [...] Read more.
The deep learning technique has already been successfully applied in the field of microwave remote sensing. Especially, convolutional neural networks have demonstrated remarkable effectiveness in synthetic aperture radar (SAR) image semantic segmentation. In this paper, a Lovász-softmax loss optimization SAR net (LoSARNet) is proposed which optimizes the semantic segmentation metric intersection over union (IOU) instead of using the traditional cross-entropy loss. Meanwhile, making use of the advantages of the dual-path structure, the network extracts feature through the spatial path (SP) and the context path (CP) to achieve a balance between efficiency and accuracy. Aiming at a polarimetric SAR (PolSAR) image, the proposed network is conducted on the PolSAR datasets for terrain segmentation. Compared to the typical dual-path network, which is the bilateral segmentation network (BiSeNet), the proposed LoSARNet can obtain better mean intersection over union (MIOU). And the proposed network also shows the highest evaluation index and the best performance when compared with several typical networks. Full article
(This article belongs to the Special Issue Target Detection with Fully-Polarized Radar)
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