Summary of S4dl: Shift-sensitive Spatial-spectral Disentangling Learning For Hyperspectral Image Unsupervised Domain Adaptation, by Jie Feng et al.
S4DL: Shift-sensitive Spatial-Spectral Disentangling Learning for Hyperspectral Image Unsupervised Domain Adaptation
by Jie Feng, Tianshu Zhang, Junpeng Zhang, Ronghua Shang, Weisheng Dong, Guangming Shi, Licheng Jiao
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 S4DL approach is a novel method for unsupervised domain adaptation in hyperspectral image (HSI) classification. It addresses the challenges of existing methods, which ignore domain information in the spectrum and struggle with varying domain shifts across datasets. The S4DL method uses gradient-guided spatial-spectral decomposition to separate domain-specific and domain-invariant representations, and a shift-sensitive adaptive monitor to adjust the intensity of disentangling based on the magnitude of domain shift. It also employs a reversible neural network to retain domain information in both semantic and shallow-level detailed information. The S4DL approach is shown to be better than state-of-the-art UDA methods through extensive experimental results on several cross-scene HSI datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to adapt computer models to new data without labels. This is useful for things like identifying different types of crops or rocks in images taken from space. The existing ways of doing this don’t work well when the images have many different bands (types) of information. The new approach, called S4DL, tries to separate the parts of the image that are specific to each place and the parts that are the same everywhere. It also adjusts how much it changes based on how different the two places are. This helps the model work better when the images have many different bands. |
Keywords
» Artificial intelligence » Classification » Domain adaptation » Neural network » Unsupervised
