Principal space approximation ensemble discriminative marginalized least-squares regression for hyperspectral image classification

Abstract

Discriminative marginalized least-squares regression (DMLSR) is unable to extract the spectral-spatial joint features, the proportion of learned interfering pixels is high. To solve this problem, a novel principal space approximation ensemble discriminant edge least-squares regression, namely PSAE-DMLSR is proposed for hyperspectral image classification. In the PSAE-DMLSR, a marginal principal component method (MP) is employed to search the optimal spectral subspace, and a principal space local marginal principal component (PSLMP) method is proposed to search the optimal representation space (ORS). In the PSLMP, a principal space representation (PSR) is designed to integrate the global spectral-spatial joint features information of the ORS, and the PSR is used to impose approximate averaging constraints and stochastic cascade fusion on the ORS, which can further improve the representation ability of the ORS. The ORS can effectively reduce the proportion of interfering pixels in DMLSR learning. It conducted comparative experiments with some more advanced classification methods on the three commonly used hyperspectral datasets. The experiment results show that the PSAE-DMLSR classification model can still obtain high classification accuracy under low hardware conditions, and the execution efficiency also has advantages.