标题： 时空频谱马尔可夫模型与稀疏可变形标记序列用于增强MODIS时间序列分类 显示英文标题

标题： Spatial-Temporal-Spectral Mamba with Sparse Deformable Token Sequence for Enhanced MODIS Time Series Classification

摘要： 尽管MODIS时间序列数据对于支持动态、大尺度的土地覆盖土地利用分类至关重要，但由于关键的MODIS困难，例如高时间维度、混合像元和时空谱耦合效应，捕捉细微的类别特征信息是一项具有挑战性的任务。 本文提出了一种新颖的时空谱Mamba（STSMamba），采用可变形标记序列以增强MODIS时间序列分类，具有以下主要贡献。 首先，为了解耦时空特征耦合，设计了一个时间分组主干（TGS）模块用于初始特征学习。 其次，为了提高Mamba建模效率和准确性，设计了一种稀疏可变形Mamba序列（SDMS）方法，可以减少Mamba序列中的潜在信息冗余，并提高Mamba序列的适应性和可学习性。 第三，基于SDMS，为了改进特征学习，设计了一种新的时空谱Mamba架构，导致三个模块，即稀疏可变形空间Mamba模块（SDSpaM）、稀疏可变形光谱Mamba模块（SDSpeM）和稀疏可变形时间Mamba模块（SDTM），以显式学习MODIS中的关键信息源。 所提出的方法在MODIS时间序列数据上进行了测试，与许多最先进的方法进行了比较，结果表明所提出的方法可以在减少计算复杂度的情况下实现更高的分类精度。 显示英文摘要

摘要： Although MODIS time series data are critical for supporting dynamic, large-scale land cover land use classification, it is a challenging task to capture the subtle class signature information due to key MODIS difficulties, e.g., high temporal dimensionality, mixed pixels, and spatial-temporal-spectral coupling effect. This paper presents a novel spatial-temporal-spectral Mamba (STSMamba) with deformable token sequence for enhanced MODIS time series classification, with the following key contributions. First, to disentangle temporal-spectral feature coupling, a temporal grouped stem (TGS) module is designed for initial feature learning. Second, to improve Mamba modeling efficiency and accuracy, a sparse, deformable Mamba sequencing (SDMS) approach is designed, which can reduce the potential information redundancy in Mamba sequence and improve the adaptability and learnability of the Mamba sequencing. Third, based on SDMS, to improve feature learning, a novel spatial-temporal-spectral Mamba architecture is designed, leading to three modules, i.e., a sparse deformable spatial Mamba module (SDSpaM), a sparse deformable spectral Mamba module (SDSpeM), and a sparse deformable temporal Mamba module (SDTM) to explicitly learn key information sources in MODIS. The proposed approach is tested on MODIS time series data in comparison with many state-of-the-art approaches, and the results demonstrate that the proposed approach can achieve higher classification accuracy with reduced computational complexity.