Subspace-based multitask learning framework for hyperspectral imagery classification

摘要

Subspace-based models have been widely applied for hyperspectral imagery applications, especially for classification. The main principle of these methods is based on the fact that the original image can approximately lie on a lower-dimensional subspace. However, due to the existence of mixed samples, the subspace projection is unstable and affected by the selection of training samples, such that may lead to poor characterization and classification performances. In order to improve the robustness and characterization ability of the subspace-based classification models, this paper proposes a novel subspace-based multitask learning framework. In particular, the original image is first projected to the multiple subspaces in different branches. Then, the support vector machine (SVM) classifier is applied in each branch to deal with the projected data sets. With a consideration of integrating the spatial information, an extended step is provided including the process of a Markov Random Field (MRF) based on the result of SVM. Finally, the classification result is obtained by a decision fusion process. Experimental results on three real hyperspectral data sets demonstrate the improvements on classification performance of the proposed methods over other related methods.