Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction

• URL: http://arxiv.org/abs/2408.04294v1
• Date: Thu, 8 Aug 2024 08:17:50 GMT
• Title: Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction
• Authors: Yuchen Wang, Ziyi Guo, Haixia Bi, Danfeng Hong, Chen Xu,
• Abstract summary: We propose a dual-branch classification model based on generative self-supervised learning in this paper. The first branch is a superpixel-branch, which learns superpixel-level polarimetric representations using a generative self-supervised graph masked autoencoder. To acquire finer classification results, a convolutional neural networks-based pixel-branch is further incorporated to learn pixel-level features.
• Score: 22.39266854681996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The annotation of polarimetric synthetic aperture radar (PolSAR) images is a labor-intensive and time-consuming process. Therefore, classifying PolSAR images with limited labels is a challenging task in remote sensing domain. In recent years, self-supervised learning approaches have proven effective in PolSAR image classification with sparse labels. However, we observe a lack of research on generative selfsupervised learning in the studied task. Motivated by this, we propose a dual-branch classification model based on generative self-supervised learning in this paper. The first branch is a superpixel-branch, which learns superpixel-level polarimetric representations using a generative self-supervised graph masked autoencoder. To acquire finer classification results, a convolutional neural networks-based pixel-branch is further incorporated to learn pixel-level features. Classification with fused dual-branch features is finally performed to obtain the predictions. Experimental results on the benchmark Flevoland dataset demonstrate that our approach yields promising classification results.

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