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Article Dans Une Revue IEEE Transactions on Geoscience and Remote Sensing Année : 2021

A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification

Bo Ren
Yangyang Zhao
  • Fonction : Auteur
Biao Hou
Licheng Jiao

Résumé

Recently, deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) data interpretation and understanding. However, for supervised methods, it requires large-scale labeled data to achieve better performance, and getting enough labeled data is a time-consuming and laborious task. Aiming to obtain a good classification result with limited labeled data, we focus on learning discriminative high-level features between multiple representations, which we call mutual information. As PolSAR data have multi-modal representations, there should have strong similarity between multi-modal features of the same pixel. In addition, each pixel has its own unique geocoding and scattering information. Hence, every pixel has great difference from other pixels in a specific representation space. Based on the above observations, this article proposes a mutual information-based self-supervised learning (MI-SSL) model to learn an implicit representation from unlabeled data. In this article, the self-supervised learning idea is first applied to PolSAR data processing. Furthermore, a reasonable pretext task, which is suitable for PolSAR data, is designed to extract mutual information for classification tasks. Compared with the state-of-the-art classification methods, experimental results on four PolSAR data sets demonstrate that our MI-SSL model produces impressive overall accuracy with fewer labeled data.
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Dates et versions

hal-03429688 , version 1 (15-11-2021)

Identifiants

Citer

Bo Ren, Yangyang Zhao, Biao Hou, Jocelyn Chanussot, Licheng Jiao. A Mutual Information-Based Self-Supervised Learning Model for PolSAR Land Cover Classification. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59 (11), pp.9224-9237. ⟨10.1109/TGRS.2020.3048967⟩. ⟨hal-03429688⟩
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