Structure Extraction With Total Variation for Hyperspectral Image Classification

This paper proposes a novel structure extraction approach that is able to achieve high classification accuracy and low computing burden to hyperspectral image (HSI) classification based on total variation (SETV). Specifically, a two-scale decomposition-based relative total variation (TSD-RTV) method is presented for the first time to process the information of different scales, such that the structure can be well extracted. Moreover, a new weighted-average fusion method is introduced, which can reduce the dimensionality and also remove the noise due to hyperspectral sensors. The support vector machine (SVM) is applied to HSI classification as a classifier. The experiments are conducted on three real hyperspectral datasets: Indian Pines, Salinas, and Kennedy Space Center. The experimental results show the outstanding performance of the proposed SETV model in terms of classification accuracy and computational efficiency.