Hyperspectral Image Classification Based on Multiscale Spectral-Spatial Deformable Network

Image classification plays a fundamental role in hyperspectral image (HSI) analysis. Since the mixed pixels of the urban areas are generally more complex than other areas, the following two problems remain to be considered while dealing with urban HSI classification: 1) due to the fact that the spectral feature of different mixed pixels varies greatly in the same class, HSI classification of urban area is susceptible to the representativeness of the training samples and 2) since the urban area is densely packed with objects of different size, the comprehensive use of the spatial and spectral features to classify the objects is a difficult problem. To tackle these problems, HSI classification based on multiscale spectral-spatial deformable network (S2-DNet) is proposed. First, a $k$ -means clustering method is adopted to cluster spectrum of each class, and representative samples are selected from the spectrum after clustering to reduce the impact of intraclass variation. Second, a spectral-spatial joint network is designed to extract the low-level features, including spectral features and spatial features. Third, the deformable network is introduced to extract high-level features of the object. Experimental results demonstrated that the proposed method outperformed the state-of-the-art methods on two widely used HSI data sets.