Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910469536612352 |
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| author | Lin, Junyan Jin, Xuepeng Gao, Feng Dong, Junyu Yu, Hui |
| author_facet | Lin, Junyan Jin, Xuepeng Gao, Feng Dong, Junyu Yu, Hui |
| contents | Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR classification methods have gradually recognized the importance of the spectral information, they have not adequately addressed the redundancy among different spectra, resulting in information leakage during the pretraining stage. This issue directly impairs the representation ability of the model. To tackle the problem, we propose a new strategy, named Mining Redundant Spectra (MRS). Unlike randomly masking spectral bands, MRS selectively masks them by similarity to increase the reconstruction difficulty. Specifically, a random spectral band is chosen during pretraining, and the selected and highly similar bands are masked. Experimental results demonstrate that employing the MRS strategy during the pretraining stage effectively improves the accuracy of existing MIM-based methods on the Berlin and Houston 2018 datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification Lin, Junyan Jin, Xuepeng Gao, Feng Dong, Junyu Yu, Hui Image and Video Processing Although recent masked image modeling (MIM)-based HSI-LiDAR/SAR classification methods have gradually recognized the importance of the spectral information, they have not adequately addressed the redundancy among different spectra, resulting in information leakage during the pretraining stage. This issue directly impairs the representation ability of the model. To tackle the problem, we propose a new strategy, named Mining Redundant Spectra (MRS). Unlike randomly masking spectral bands, MRS selectively masks them by similarity to increase the reconstruction difficulty. Specifically, a random spectral band is chosen during pretraining, and the selected and highly similar bands are masked. Experimental results demonstrate that employing the MRS strategy during the pretraining stage effectively improves the accuracy of existing MIM-based methods on the Berlin and Houston 2018 datasets. |
| title | Boosting Spatial-Spectral Masked Auto-Encoder Through Mining Redundant Spectra for HSI-SAR/LiDAR Classification |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2406.01235 |