Dynamic Global–Local Selection Network for Hyperspectral Image Classification
The newly introduced Mamba architecture exhibits superior performance in the hyperspectral image (HSI) classification domain, as it achieves long-range dependence modeling with linear complexity and is widely regarded as a promising alternative to the transformer. However, existing Mamba-based HSI classification methods still face two major limitations: first, fixed multidirectional scanning paths lack content adaptability and introduce computational redundancy; second, the sequential scanning process disrupts intrinsic 2-D local structures, impairing fine-grained spatial features. To overcome these issues, we propose a novel dynamic global–local selection network (DGLSN), which synergistically integrates Mamba and a convolutional neural network within an input-adaptive multibranch framework. Our approach introduces two core modules: a global dynamic selection module that employs a lightweight decision network to activate only the most relevant Mamba blocks for efficient long-range modeling, and a local dynamic selection module that dynamically selects among spatial-, spectral-, and frequency-domain convolutional branches to extract discriminative multiview features. Extensive experiments demonstrate that DGLSN achieves state-of-the-art classification accuracy while requiring competitive training and testing times and significantly lower floating point operations, highlighting its superiority in both performance and computational efficiency.