Triple-Regularized Latent Subspace Discriminative Regression for Hyperspectral Image Classification

Abstract

There are various types and distributions of noise in hyperspectral images. However, the existing classification models are vulnerable to noise in the data. To improve the robustness of the classification models, a novel hyperspectral image classification model is proposed, named triple-regularized latent subspace discriminative regression (TRLSDR). The core idea of TRLSDR is to add a latent subspace to the standard discriminative least squares regression model to extract high-order features from the visual space by undercomplete autoencoder, and then use clean data for classification. Three regularizers are introduced in this process: Tikhonov regularizer is used to avoid overfitting; Laplacian regularizer is used to capture the neighborhood relationship; and low-rank regularizer is used to alleviate the error of Laplacian matrix construction caused by noise in original samples. We designed experiments on five hyperspectral image datasets and the results show that the proposed model is superior to the existing regression models.

Keywords

• Graph regularization
• hyperspectral image classification
• low rank
• manifold learning
• noise reduction