Visual Saliency-Based Extended Morphological Profiles for Unsupervised Feature Learning of Hyperspectral Images

Abstract

Classification of hyperspectral images (HSIs) by making full use of the spectral and the spatial information has become a research hotspot in the field of remote sensing technology. Aiming at the problems of information redundancy and low utilization of spatial information, this letter proposes a visual saliency-based extended morphological profile (VS-EMP) scheme. First, the morphological features are extracted by the EMP from the HSIs on several principal components. Second, the local binary pattern (LBP) is performed to extract the texture features from morphological scenes. Third, saliency features are captured according to the texture features in an approach of Boolean mapping saliency (BMS). Finally, spectral-spatial features are constructed by feature fusion and are further used for the classification of the HSIs. A number of experiments are performed, including using different classifiers to verify the performance of the proposed scheme, comparing with related variant algorithms, comparing time with deep learning, and testing learning ability in the absence of labeled samples. Experimental results indicate that the proposed method is significantly superior to the previous methods.