Representative Spectral Correlation Network for Multi-source Remote Sensing Image Classification
Hyperspectral image (HSI) and SAR/LiDAR data offer complementary spectral and structural information for land-cover classification. However, their effective fusion remains challenging due to two major limitations: The spectral redundancy in high-dimensional HSI and the heterogeneous characteristics between multi-source data. To this end, we propose Representative Spectral Correlation Network (RSCNet), a novel multi-source image classification framework specifically designed to address the above challenges through spectral selection and adaptive interaction. The network incorporates two key components: (1) Key Band Selection Module (KBSM) that adaptively selects task-relevant spectral band...
| Authors | Chuanzheng Gong, Feng Gao, Junyan Lin et al. |
|---|---|
| Primary category | eess.IV |
| Published | 30 April 2026 |
| Last updated at source | 30 April 2026 |
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