An Efficient Algorithm for Hyperspectral Image Clustering

Hyperspectral images (HSIs) clustering problem is a challenge and valuable task due to its inherent complexity and abundant spectral information. Sparse subspace clustering (SSC) and SSC-based methods are widely used in this problem and demonstrate excellent performance. However, considering that HSIs are usually of high dimension, these methods have expensive computing complexity because of the usage of SSC. To solve this problem, we propose a novel approach called SuperPixel and Angle-based HyperSpectral Image Clustering (SPAHSIC). It first extracts the local spectral and spatial information between pixels by superpixel segmentation, and then applies spectral clustering on the similarity matrix built based on subspace principal angles. We implement experiments on real datasets and get a high accuracy, which indicates the effectiveness of our algorithm.