Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911203581755392 |
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| author | Xu, Zhengsen Zhu, Yimin Dewis, Zack Heffring, Mabel Alkayid, Motasem Taleghanidoozdoozan, Saeid Xu, Lincoln Linlin |
| author_facet | Xu, Zhengsen Zhu, Yimin Dewis, Zack Heffring, Mabel Alkayid, Motasem Taleghanidoozdoozan, Saeid Xu, Lincoln Linlin |
| contents | Although change detection using MODIS time series is critical for environmental monitoring, it is a highly challenging task due to key MODIS difficulties, e.g., mixed pixels, spatial-spectral-temporal information coupling effect, and background class heterogeneity. This paper presents a novel knowledge-aware Mamba (KAMamba) for enhanced MODIS change detection, with the following contributions. First, to leverage knowledge regarding class transitions, we design a novel knowledge-driven transition-matrix-guided approach, leading to a knowledge-aware transition loss (KAT-loss) that can enhance detection accuracies. Second, to improve model constraints, a multi-task learning approach is designed, where three losses, i.e., pre-change classification loss (PreC-loss), post-change classification loss (PostC-loss), and change detection loss (Chg-loss) are used for improve model learning. Third, to disentangle information coupling in MODIS time series, novel spatial-spectral-temporal Mamba (SSTMamba) modules are designed. Last, to improve Mamba model efficiency and remove computational cost, a sparse and deformable Mamba (SDMamba) backbone is used in SSTMamba. On the MODIS time-series dataset for Saskatchewan, Canada, we evaluate the method on land-cover change detection and LULC classification; results show about 1.5-6% gains in average F1 for change detection over baselines, and about 2% improvements in OA, AA, and Kappa for LULC classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09679 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series Xu, Zhengsen Zhu, Yimin Dewis, Zack Heffring, Mabel Alkayid, Motasem Taleghanidoozdoozan, Saeid Xu, Lincoln Linlin Computer Vision and Pattern Recognition Although change detection using MODIS time series is critical for environmental monitoring, it is a highly challenging task due to key MODIS difficulties, e.g., mixed pixels, spatial-spectral-temporal information coupling effect, and background class heterogeneity. This paper presents a novel knowledge-aware Mamba (KAMamba) for enhanced MODIS change detection, with the following contributions. First, to leverage knowledge regarding class transitions, we design a novel knowledge-driven transition-matrix-guided approach, leading to a knowledge-aware transition loss (KAT-loss) that can enhance detection accuracies. Second, to improve model constraints, a multi-task learning approach is designed, where three losses, i.e., pre-change classification loss (PreC-loss), post-change classification loss (PostC-loss), and change detection loss (Chg-loss) are used for improve model learning. Third, to disentangle information coupling in MODIS time series, novel spatial-spectral-temporal Mamba (SSTMamba) modules are designed. Last, to improve Mamba model efficiency and remove computational cost, a sparse and deformable Mamba (SDMamba) backbone is used in SSTMamba. On the MODIS time-series dataset for Saskatchewan, Canada, we evaluate the method on land-cover change detection and LULC classification; results show about 1.5-6% gains in average F1 for change detection over baselines, and about 2% improvements in OA, AA, and Kappa for LULC classification. |
| title | Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.09679 |