Summary of Spectralearth: Training Hyperspectral Foundation Models at Scale, by Nassim Ait Ali Braham et al.
SpectralEarth: Training Hyperspectral Foundation Models at Scale
by Nassim Ait Ali Braham, Conrad M Albrecht, Julien Mairal, Jocelyn Chanussot, Yi Wang, Xiao Xiang Zhu
First submitted to arxiv on: 15 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 This paper introduces SpectralEarth, a large-scale multi-temporal dataset for hyperspectral imaging (HSI) pretraining foundation models. Leveraging data from the Environmental Mapping and Analysis Program (EnMAP), the dataset comprises 538,974 image patches covering 415,153 unique locations from over 11,636 scenes spanning two years of archive. The authors also propose a spectral adapter to classical vision backbones for accommodating HSI characteristics and demonstrate the versatility of their models through state-of-the-art self-supervised learning (SSL) algorithms. Four downstream datasets are constructed for land-cover and crop-type mapping, providing benchmarks for model evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SpectralEarth is a big dataset that helps computers learn about the world from hyperspectral images. These images have lots of information about what things look like in different colors. The authors made this dataset to help computers learn about this type of image without needing a lot of human training. They also created special ways for computers to understand these images and showed that these methods work well on different tasks. |
Keywords
» Artificial intelligence » Pretraining » Self supervised
