Dynamic Global–Local Selection Network for Hyperspectral Image Classification
TL;DR
A novel dynamic global–local selection network (DGLSN), which synergistically integrates Mamba and a convolutional neural network within an input-adaptive multibranch framework, which achieves state-of-the-art classification accuracy while requiring competitive training and testing times and significantly lower floating point operations.
Abstract
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.