Superpixel-Level Global and Local Similarity Graph-Based Clustering for Large Hyperspectral Images

Due to the scarcity of labeled samples, clustering in hyperspectral images (HSIs) has a great potential and application value. However, current clustering methods are mainly pixel-level techniques that neglect the large spectral variability of a scene and suffer from massive time and memory consumption when dealing with large HSIs. In this article, we propose a superpixel-level global and local similarity graph-based clustering (SGLSC) algorithm that can classify ground objects exploiting spectral and spatial dimensions with reasonable time and memory consumption on large HSIs. The proposed SGLSC exploits the superpixel concept, which is treated as a homogeneous entity, into the clustering process. For modeling the essential structure of HSIs, a similarity graph combing the global and local information is constructed and inserted into the spectral clustering to partition the superpixel-level graph structure. The proposed method was tested on three benchmark HSIs’ datasets and compared with some advanced literature algorithms. Experiments demonstrate that it can obtain promising results.