TS-SHES: Terrain Segmentation in Complex-Valued PolSAR Images Via Scattering Harmonization and Explicit Supervision

Xuan Zeng, Zhirui Wang, Ke Feng, Xin Gao, Xian Sun

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Convolutional neural network (CNN) has attracted extensive attention in the research field of polarimetric synthetic aperture radar (PolSAR) terrain segmentation. However, directly using CNN in PolSAR terrain segmentation while ignoring the characteristics of PolSAR images has become the main factor restricting the performance of algorithms. In this article, we propose an efficient PolSAR terrain segmentation algorithm called terrain segmentation in complex-valued PolSAR images via scattering harmonization and explicit supervision (TS-SHES), which integrates the polarization scattering characteristics of PolSAR images and the CNN learning process into a unified architecture. First, considering the intrinsic structure of the complex-valued PolSAR data, TS-SHES transforms the scattering matrix into the form of amplitude and phase components, which preserves the original information maximally. Then, TS-SHES introduces a scattering harmonized encoding (SH-Enc) method to balance the feature contributions of weak and strong scattering regions as well as map the two components into the same representation space. Through the above scattering harmonization operations, the segmentation performance of CNN on weak scattering regions can be improved, and the feature imbalance in amplitude and phase can be alleviated. Furthermore, in view of the implicit states of CNN feature construction, a scattering explicit learning network (SEL-Net) is presented to collect the scattering features of amplitude and phase. Via explicit supervision, SEL-Net avoids incomplete collection of scattering information caused by implicit feature construction, thereby improving the segmentation accuracy. Abundant experiments are conducted on two PolSAR images acquired by the GaoFen-3 satellite, which demonstrates the superiority of our proposed algorithm.
Original languageEnglish (US)
JournalIEEE Transactions on Geoscience and Remote Sensing
StatePublished - Jan 1 2022
Externally publishedYes

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Generated from Scopus record by KAUST IRTS on 2023-09-21


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