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arXiv 2510.09679cs.CV

基于知识感知Mamba的MODIS时间序列联合变化检测与分类

Knowledge-Aware Mamba for Joint Change Detection and Classification from MODIS Times Series

Department of Geomatics Engineering, University of Calgary, Calgary, Canada(测绘工程系,卡尔加里大学,卡尔加里,加拿大)

Zhengsen Xu, Yimin Zhu, Zack Dewis, Mabel Heffring, Motasem Alkayid, Saeid Taleghanidoozdoozan, Lincoln Linlin Xu

AI总结:

本文提出知识感知Mamba(KAMamba)框架,通过知识驱动转换矩阵、多任务学习及空间-光谱-时间Mamba模块,有效解耦MODIS时间序列信息,显著提升了土地覆盖变化检测与LULC分类的精度。

摘要:

尽管使用MODIS时间序列进行变化检测对环境监测至关重要,但由于MODIS的关键困难(例如混合像素、空间-光谱-时间信息耦合效应以及背景类别异质性),这是一项极具挑战性的任务。本文提出了一种新颖的知识感知Mamba(KAMamba)用于增强MODIS变化检测,其贡献如下。首先,为了利用关于类别转换的知识,我们设计了一种新颖的知识驱动的转换矩阵引导方法,从而产生知识感知转换损失(KAT-loss),可以提高检测精度。其次,为了改善模型约束,设计了一种多任务学习方法,其中使用三个损失,即变化前分类损失(PreC-loss)、变化后分类损失(PostC-loss)和变化检测损失(Chg-loss)来改善模型学习。第三,为了解耦MODIS时间序列中的信息耦合,设计了新颖的空间-光谱-时间Mamba(SSTMamba)模块。最后,为了提高Mamba模型效率并消除计算成本,在SSTMamba中使用了稀疏可变形Mamba(SDMamba)主干。在加拿大萨斯喀彻温省的MODIS时间序列数据集上,我们评估了该方法在土地覆盖变化检测和LULC分类上的表现;结果显示,与基线相比,变化检测的平均F1提高了约1.5-6%,LULC分类的OA、AA和Kappa提高了约2%。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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