Extended Semantic Edge-Aware Filtering Profile for Hyperspectral Image Classification

Spectral-spatial classification of hyperspectral images (HSIs) has been extensively studied. Although the importance of spatial information for the classification of HSIs is widely proven in the literature, the definition of effective techniques for the extraction of spatial information is still a challenging and open research issue. In this letter, a semantic edge-aware structure-preserving image filtering technique is presented to accurately model spatial information in HSI classification. The experimental results on the three real HSI datasets show the superiority of our model, which provides at least 2% higher classification accuracy than the best among the numerous literature models considered.