Robust subspace learning method for hyperspectral image classification

Abstract

Subspace learning (SL) is an important technology to extract the discriminative features for hyperspectral image (HSI) classification. However, in practical applications, some acquired HSIs are contaminated with considerable noise during the imaging process. In this case, most of existing SL methods yield limited performance for subsequent classification procedure. In this paper, we propose a robust subspace learning (RSL) method, which utilizes a local linear regression and a supervised regularization function simultaneously. To effectively incorporate the spatial information, a local linear regression is used to seek the recovered data from the noisy data under a spatial set. The recovered data not only reduce the noise effect but also include the spectral-spatial information. To utilize the label information, a supervised regularization function based on the idea of Fisher criterion is used to learn a discriminative subspace from the recovered data. To optimize RSL, we develop an efficient iterative algorithm. Extensive experimental results demonstrate that RSL greatly outperforms many existing SL methods when the HSI data contain considerable noise.