Collaborative Spectral–Spatial Representation Learning for Hyperspectral and LiDAR Classification Under Limited Samples

Hyperspectral images (HSI) offer exceptional precision in distinguishing features due to their broad spectral dimensions. However, their high dimensionality gives rise to a phenomenon known as the ‘dimensional curse’, characterized by data sparsity in high-dimensional feature spaces. This issue is further exacerbated by the limited number of labeled samples, rendering it challenging to effectively define the decision boundary and increasing risk of over-fitting. To address the challenges, we propose a spectral-spatial representation learning framework based on hyperspectral image and light detection and ranging (LiDAR) data, which enhances the generalization of spectral features while reducing dimensionality through the optimization of spectral-wise information. Meanwhile, a local-global spatial feature fusion mechanism is designed for LiDAR spatial features to further alleviating the sparsity of spectral features and to effectively recognize complex land cover. The method fully leverages the complementary strengths of HSI and LiDAR data through self-supervised contrastive learning, effectively mitigates the challenge posed by data properties. Extensive experiments were conducted on three widely used HSI-LiDAR datasets, and the results demonstrate that the proposed algorithm outperforms state-of-art methods in classification accuracy.