Locally Constrained Collaborative Representation Based Fisher's LDA for Clustering of Hyperspectral Images

Clustering of hyperspectral images (HSIs) is a challenging task, due to high dimensional, unbalance data distribution and complex spectral-spatial structures. Existing methods mainly use either data spectral attribute only or spatial similarity only without gathering them appropriately. In this paper, under an extended linear discriminant analysis (LDA) framework, a new method, locally constrained collaborative representation based Fisher's LDA for clustering (LCR-FLDA), is proposed for the clustering of HSIs. We propose a New Fisher's LDA (FLDA) model by optimizing the integration of K-means-Laplacian and N-cut clustering to fully utilize the spectral-spatial discriminative representation information both in data and feature domain. Then, using locally constrained collaboration representation method to build a similarity matrix in FLDA to incorporate both local relationship structure and attribute information. Experimental results were conducted to illustrate the effectiveness of the proposed method.