Discriminative Marginalized Least-Squares Regression for Hyperspectral Image Classification

Least-squares regression (LSR)-based classifiers are effective in multiclassification tasks. However, most existing methods use limited projections, resulting in loss of much discriminant information; furthermore, they focus only on exactly fitting samples to target matrix while ignoring overfitting issue. To solve these drawbacks, discriminative marginalized LSR (DMLSR) is proposed to learn a more discriminative projection matrix with consideration of class separability and data-reconstruction ability simultaneously. In the proposed framework, an intraclass compactness graph is employed to avoid the overfitting problem and enhance class separability, and a data-reconstruction constraint is imposed to preserve discriminant information on limited projections. Experimental results on several hyperspectral data sets demonstrate that the proposed method significantly outperforms some state-of-the-art classifiers.

I. Introduction

Hyperspectral imagery (HSI) have wideband range and are used to identify different vegetation and materials [1]–[6]. Based on rich spectral signatures, numerous classification techniques have been investigated [7], [8] and used in many remote sensing applications, such as urban mapping and environment pollution monitoring [9]–[13].