GICL-Net: A graph-interactive collaborative learning network for joint classification of HSI and LiDAR

Abstract

Hyperspectral and LiDAR images, as a class of multi-source remote sensing data, have their own advantageous features. HSI contains rich spectral information, compared to LiDAR, which is more capable of reflecting the height information of the target area. However, determining the intrinsic correlation characteristics between HSI channels and fully exploiting the complementary characteristics of HSI and LiDAR to improve classification accuracy remains a challenging issue. In this paper, we propose a graph-interactive collaborative learning network (GICL-Net) for joint classification of HSI and LiDAR, which consists of a multi-scale spectral-spatial graph feature extractor (MSGE) and a cross-modality adjacency matrix fusion (CAMF) module. Specifically, MSGE is divided into two branches. One branch starts from the spectral dimension, defining each channel of HSI as a node to construct a graph network and mine potential correlations at the channel level, thereby capturing the intrinsic spectral correlation features of the target region. The other branch focuses on the height information of the target area, constructing a graph network at the geometric spatial level to extract representative height features. The CAMF module leverages the fused adjacency matrix to establish a cross-modal graph interaction mechanism, enabling the spectral features and the spatial features to achieve deep synergy through iterative updates in graph convolution. This generates cross-modal fusion features with fine spectral discrimination capability and spatial dimensional structural integrity. This method has been validated for effectiveness across multiple HSI and LiDAR datasets. In particular, on the Houston2013 dataset, the overall accuracy (OA) reached 98.91%, with a Kappa coefficient of 0.9882. Furthermore, compared with the baseline network (i.e., the conventional two-branch graph network, the overall accuracy improved by 6.58%. Our code is available at the link: https://github.com/Xidian-AIGroup190726/GICLNet.