Abstract
Jointly exploiting spectral information and spatial information, rather than working on individual pixels, is important for hyperspectral target detection. In this letter, we propose a hyperspectral target detection method relying on superpixel structures of the input image. Multiscale superpixels are generated to capture textures of the image, and each superpixel is summarized to its representative, which is the average of all its pixels. The SparseCEM detector is then applied to these representatives. Finally, the detection results from all scales are fused to achieve the final output. Our experiment results show that the multiscale-superpixel-based SparseCEM detector (MSSD) outperforms the compared typical detection methods.
Original language | English |
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Journal | IEEE Geoscience and Remote Sensing Letters |
Volume | 19 |
DOIs | |
State | Published - 2022 |
Keywords
- Constrained energy minimization (CEM)
- hyperspectral target detection
- multiscale
- object level
- superpixel