Decorrelation–Separability-Based Affinity Propagation for Semisupervised Clustering of Hyperspectral Images

Abstract

Dimensionality reduction is a common approach to decrease the high computational complexity and improve the classification performance of hyperspectral images. The paper addresses this issue by introducing a novel semisupervised clustering approach to hyperspectral image classification. In this approach, the relationships between the samples (i.e., pixels in hyperspectral data) are measured by two kinds of side constraints, i.e., positive and negative constraints, which are imposed to construct a discriminative transformation that establishes a regularized metric function. Accordingly, a new subspace is built in which the class discrimination capability of each individual feature is expanded, while the spectral correlation among features is greatly reduced. Then, the learned metric is formulated within an exemplar-based clustering framework, i.e., the affinity propagation (AP). Thus, the proposed approach is called decorrelation-separability-based AP (DS-AP). Experimental results obtained on three hyperspectral remote sensing data sets demonstrate the effectiveness of the proposed DS-AP technique for hyperspectral image classification.