Summary of Dual-branch Polsar Image Classification Based on Graphmae and Local Feature Extraction, by Yuchen Wang et al.
Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction
by Yuchen Wang, Ziyi Guo, Haixia Bi, Danfeng Hong, Chen Xu
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed dual-branch classification model utilizes generative self-supervised learning for PolSAR image classification with limited labels. It consists of a superpixel-branch and a pixel-branch, which learn polarimetric representations and features respectively using graph masked autoencoders and convolutional neural networks. The model is evaluated on the Flevoland dataset, demonstrating promising classification results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses artificial intelligence to help analyze satellite images of the Earth’s surface. The method is designed to work with limited labels, which can be a challenge in this field. The approach combines two types of learning: superpixel-level and pixel-level features are learned using different techniques. This combination allows for more accurate predictions than using just one type of feature. |
Keywords
» Artificial intelligence » Classification » Image classification » Self supervised
