用超像素提升遥感影像分类精度，兼顾细节与全局信息
Multitask GLocal OBIA-Mamba for Sentinel-2 Landcover Mapping
- 以超像素为输入，降低计算冗余同时保留细粒度特征
- 双分支结构融合局部细节与全局上下文，提升分类一致性
- 多任务优化平衡局部精度与整体一致性，适合高精度遥感制图
基于哨兵-2的用地和土地覆盖（LULC）分类对环境监测至关重要，但受空间异质性、上下文信息缺失和光谱混淆等数据挑战影响，仍具难度。本文提出一种新型多任务全局-局部对象基图像分析Mamba模型（MSOM），通过三个关键贡献提升性能：首先，设计基于对象的图像分析Mamba模型（OBIA-Mamba），利用超像素作为Mamba令牌，减少冗余计算而不损失细粒度信息；其次，构建全局-局部（GLocal）双分支卷积神经网络与Mamba融合架构，联合建模局部空间细节与全局上下文信息；第三，引入多任务优化框架，采用双重损失函数平衡局部精度与全局一致性。在加拿大阿尔伯塔省的哨兵-2影像上进行测试，结果表明该方法在分类准确率和细节表现上均优于多种先进方法。
原文摘要 · Abstract (English)
Although Sentinel-2 based land use and land cover (LULC) classification is critical for various environmental monitoring applications, it is a very difficult task due to some key data challenges (e.g., spatial heterogeneity, context information, signature ambiguity). This paper presents a novel Multitask Glocal OBIA-Mamba (MSOM) for enhanced Sentinel-2 classification with the following contributions. First, an object-based image analysis (OBIA) Mamba model (OBIA-Mamba) is designed to reduce redundant computation without compromising fine-grained details by using superpixels as Mamba tokens. Second, a global-local (GLocal) dual-branch convolutional neural network (CNN)-mamba architecture is designed to jointly model local spatial detail and global contextual information. Third, a multitask optimization framework is designed to employ dual loss functions to balance local precision with global consistency. The proposed approach is tested on Sentinel-2 imagery in Alberta, Canada, in comparison with several advanced classification approaches, and the results demonstrate that the proposed approach achieves higher classification accuracy and finer details that the other state-of-the-art methods.
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