MambaHSINet: A Dual-Branch Bidirectional State Space Network for Hyperspectral Tree Species Classification

Hyperspectral remote sensing provides rich spectral information and has been widely used in fine-grained land-cover classification and forest monitoring. However, accurate tree species classification remains challenging due to subtle interspecific spectral differences, similar spatial structures among related species, redundant spectral bands, and the limited ability of existing methods to model long-range spatial–spectral dependencies efficiently. In addition, many existing hyperspectral image classification methods rely on patch-based inputs and sliding-window inference, which often lead to redundant computation and insufficient utilization of global image context. To address these issues, this paper proposes MambaHSINet, a dual-branch bidirectional state space network for full-image pixel-wise hyperspectral classification. Specifically, the proposed network employs a spectral branch and a spatial branch to explicitly extract complementary spectral responses and spatial structural features. Subsequently, a bidirectional Mamba global modeling module based on selective state space modeling is adopted to capture long-range contextual dependencies in both forward and backward directions with linear computational complexity. Unlike conventional patch-based methods, MambaHSINet takes the entire hyperspectral image as input and produces full-resolution pixel-wise classification maps, thereby avoiding repeated cropping and redundant sliding-window inference. We also construct two well-annotated subsets of a UAV-borne hyperspectral dataset dedicated to tree species classification. Experimental results on self-collected and public hyperspectral datasets demonstrate that the proposed method achieves excellent classification accuracy, inference efficiency, and generalization performance. It exhibits great potential for practical tree species classification and other general hyperspectral application scenarios.