Hyperspectral Image Classification with Multi-Path 3D-CNN and Coordinated Hierarchical Attention

Convolutional Neural Networks (CNNs) have been extensively applied for the extraction of deep features in hyperspectral imagery tasks. However, traditional 3D-CNNs are limited by their fixed-size receptive fields and inherent locality. This restricts their ability to capture multi-scale objects and model long-range dependencies, ultimately hindering the representation of large-area land-cover structures. To overcome these drawbacks, we present a new framework designed to integrate multi-scale feature fusion and a hierarchical attention mechanism for hyperspectral image classification. Channel-wise Squeeze-and-Excitation (SE) and Convolutional Block Attention Module (CBAM) spatial attention are combined to enhance feature representation from both spectral bands and spatial locations, allowing the network to emphasize critical wavelengths and salient spatial structures. Finally, by integrating the self-attention inherent in the Transformer architecture with a Cross-Attention Fusion (CAF) mechanism, a local-global feature fusion module is developed. This module effectively captures extended-span interdependencies present in hyperspectral remote sensing images, and this process facilitates the effective integration of both localized and holistic attributes. On the Salinas Valley dataset, the proposed method delivers an Overall Accuracy (OA) of 0.9929 and an Average Accuracy (AA) of 0.9949, attaining perfect recognition accuracy for certain classes. The proposed model demonstrates commendable class balance and classification stability. Across multiple publicly available hyperspectral remote sensing image datasets, it systematically produces classification outcomes that significantly outperform those of established benchmark methods, exhibiting distinct advantages in feature representation, structural modeling, and the discrimination of complex ground objects.