Abstract
Recently, superpixel segmentation has been widely employed in hyperspectral image (HSI) classification of remote sensing. However, the structures of land-covers in HSI commonly vary greatly, which makes it difficult to fully fit the boundaries of land-covers by single-scale superpixel segmentation. Moreover, the shape-irregularity of superpixel brings challenge for depth feature extraction. To overcome these issues, a multiscale superpixel depth feature extraction (MSDFE) method is proposed for HSI classification in this article, which effectively explores and integrates the spatial-spectral information of land-covers by adopting multiscale superpixel segmentation, constructing statistical features of superpixel, and conducting depth feature extraction. Specifically, to exploit rich spatial information of HSI, multiscale superpixel segmentation is firstly applied on the HSI. Once superpixels on different scales are obtained, two-dimensional statistical features with a united form are constructed for these superpixels with different spatial shapes. Based on these two-dimensional statistical features, a convolutional neural network is utilized to learn deeper features and classify these depth features. Finally, an adaptive strategy is adopted to fuse the multiscale classification results. Experiments on three real hyperspectral datasets indicate the superiority of the proposed MSDFE method over several state-of-the-art methods.