← 研踪 PaperTrailarXiv:2611.03069 · v1
Shared-Pattern Collaborative Network for Multi-Source Remote Sensing Data Classification
首次提交 2026-11-02 20:56
版本更新 2026-11-02 20:56
雷达观测 2026-11-04 23:41
摘要
Multi-source remote sensing data classification has received growing attention recently. However, existing methods still struggle to jointly model long-range contextual dependencies and shared spatial patterns across heterogeneous modalities. To address this issue, we propose a Shared-Pattern Collaborative Network (SPCNet) for HSI and SAR/LiDAR joint classification. To enhance cross-modal collaborative learning, Shared-Pattern Collaborative Block (SPCB) is designed by introducing a shared large-kernel operator and a dynamic convolution branch, enabling the network to capture common spatial structures and content-adaptive local features. Furthermore, Region-Adaptive Modality Allocator (RAMA) is developed to assign spatially varying modality weights for reliable feature fusion. Experiments on Houston2013, Augsburg, and Houston2018 datasets demonstrate that SPCNet achieves superior classification performance over several state-of-the-art methods. The code will be made publicly available at https://github.com/oucailab/SPCNet
作者备注：Accepted for publication in IEEE JSTARS 2026
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v1 · 2026-11-02 20:56 · 最新观测版本
Shared-Pattern Collaborative Network for Multi-Source Remote Sensing Data Classification
Multi-source remote sensing data classification has received growing attention recently. However, existing methods still struggle to jointly model long-range contextual dependencies and shared spatial patterns across heterogeneous modalities. To address this issue, we propose a Shared-Pattern Collaborative Network (SPCNet) for HSI and SAR/LiDAR joint classification. To enhance cross-modal collaborative learning, Shared-Pattern Collaborative Block (SPCB) is designed by introducing a shared large-kernel operator and a dynamic convolution branch, enabling the network to capture common spatial structures and content-adaptive local features. Furthermore, Region-Adaptive Modality Allocator (RAMA) is developed to assign spatially varying modality weights for reliable feature fusion. Experiments on Houston2013, Augsburg, and Houston2018 datasets demonstrate that SPCNet achieves superior classification performance over several state-of-the-art methods. The code will be made publicly available at https://github.com/oucailab/SPCNet
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