Center-Aware Global-to-Local Modeling for Patch-Based HSI–LiDAR Classification

Abstract

Hyperspectral imagery (HSI) and light detection and ranging (LiDAR) provide complementary spectral and elevation cues for fine-grained land-cover classification, yet accurate pixel-wise fusion remains challenging in heterogeneous scenes. Most deep HSI–LiDAR classifiers follow a center-supervised patch-based setting, where supervision is defined on the center pixel while predictions are inferred from a context-enhanced patch representation. Under this setting, mixed semantics within a patch induce heterogeneous-patch ambiguity: contextual pixels exert uneven influence on the center-pixel decision, and indiscriminate context aggregation can dilute center-consistent evidence in mixed and boundary regions. To address this issue, we propose a center-aware global-to-local refinement framework that explicitly regulates how contextual information is organized and accumulated under center supervision. First, a lightweight cross-modal channel alignment module fuses HSI and LiDAR features into a unified representation while preserving the spatial layout. Second, we introduce a center-aware global regulation module built on Vision Mamba, equipped with a dual-direction Spiral Scan that orders tokens in a periphery-to-center manner. This design induces a structured information flow that progressively consolidates center-relevant semantics under heterogeneous neighborhoods. Finally, a lightweight spatial–spectral refinement module (SSRM) refines discriminative local details within the globally regulated feature space by recovering boundary-sensitive structures and recalibrating channel responses. Extensive experiments on three public benchmarks (Houston2013, MUUFL, and Augsburg) demonstrate that the proposed method consistently outperforms representative local neighborhood modeling, within-patch global interaction, and local–global hybrid approaches. The code is available at https://github.com/lmwdhr/ViT–CNN