Joint Classification Network via Superpixel-Guided Prototypical Feature for Hyperspectral and LiDAR Data

Abstract

In recent years, convolutional neural networks-based methods have been extensively applied to hyperspectral image (HSI) and light detection and ranging (LiDAR) joint classification. However, most existing methods rely on fixed sliding windows for local feature extraction, making them susceptible to heterogeneous pixel interference, particularly around object boundaries and detail-rich regions. Moreover, their performance often degrades due to the limited availability of labeled samples. To address these limitations, this article proposes a joint classification network via superpixel-guided prototypical feature. Specifically, a collaborative superpixel segmentation strategy is first employed by integrating the spectral–spatial characteristics of HSI with the geometric information of LiDAR, enabling the extraction of homogeneous regions. Based on these regions, an adaptive patch restructuring strategy is introduced to generate regular-shape patches. Subsequently, these regular-shape patches are fed into a dual-branch multiscale convolutional module for hierarchical feature extraction and cross-modal fusion. Finally, class prototypes are constructed in the embedding space, and the Mahalanobis distance is adopted as the similarity metric for classification. Experimental results on three benchmark datasets demonstrate the propose SPFNet achieves competitive classification performance, particularly in complex scenes and few-shot scenarios.