Modified Extinction Profiles for Hyperspectral Image Classification

Spectral-Spatial features are helpful for hyperspectral image classification. One of the most successful approaches based morphology is Extinction Profiles (EPs), which is constructed based on the component trees (Max-tree/Mintree) and can accurately extract spatial and contextual information from remote sensing images. However, the dimension of feature extracted by EPs with component trees is large, which potentially causes high redundancy. In order to reduce redundancy information and achieve better feature extraction, we propose a modified EP with the Topological trees (Inclusion tree). The proposed method is carried out on two commonlyused hyperspectral datasets captured over North-western Indiana and Salinas, California. The results show that the proposed method has significantly improved in terms of both accuracy and complexity on the basis of a reduction of half of the feature dimensions compared to the original EPs.