Scalable Graph-Based Clustering With Nonnegative Relaxation for Large Hyperspectral Image

Hyperspectral image (HSI) clustering is very important in remote sensing applications. However, most graph-based clustering models are not suitable for dealing with large HSI due to their computational bottlenecks: the construction of the similarity matrix W, the eigenvalue decomposition of the graph Laplacian matrix L, and k-means or other discretization procedures. To solve this problem, we prop...