Dimensionality-Reduced Spatial Bipartite Graph Clustering for Hyperspectral and LiDAR Data

Abstract

The growing volume of remote sensing (RS) data highlights the need for enhanced data integration and processing. While combining hyperspectral and LiDAR data improves analysis by addressing spectral variability, challenges persist due to the high dimensionality, noise, and outliers in hyperspectral images (HSI). Additionally, supervised classification is labor-intensive, further motivating the need for advanced unsupervised clustering methods. Current clustering approaches, however, struggle with underutilization of spatial information, redundant spectral bands, and information divergence across multimodal data. To overcome these issues, we propose a Dimensionality-Reduced Spatial Bipartite Graph Clustering for Hyperspectral and LiDAR Data. This method integrates spatial information through bipartite graphs, reduces dimensionality by eliminating redundant bands, and employs a tensor-based framework to explore consistent structures in the low-rank space. This reduces information divergence and enhances clustering stability and performance. Extensive experiments demonstrate the effectiveness and robustness of the proposed method on real datasets.