Hyperspectral Classification Based on Coupling Multiscale Super-Pixels and Spatial Spectral Features

Hyperspectral remotely sensed images contain not only the spatial information of ground objects, but also their rich spectral information. Effectively improve the classification accuracy of hyperspectral images (HSIs), the purpose is to accurately grasp the current land resource utilization information, which is of great significance to the formulation and implementation of future land and space planning. Existing research on the classification of hyperspectral remotely sensed images uses a single-scale superpixel method for image segmentation. The optimal number of superpixels cannot be determined, image details may get missed, and a single kernel matrix cannot represent the information from multiple features, which results in reduced classification accuracy. Therefore, this study intends to use the superpixel segmentation method to perform multiscale superpixel segmentation on the principal components of HSIs at multiple scales, and to couple the multiscale superpixel spatial spectral kernel (SSK) with the original spectral kernel to form a synthetic kernel using weights for HSI classification. The three HSIs of Pavia University, Pavia Center, and Washington DC Mall are used as the experimental data to test and analyze this method. The experimental results show that the effective classification accuracies for the three datasets obtained by this method are 5.28%, 5.90%, and 7.71% higher than the five comparison methods, at best. The results prove that this method can effectively solve the problem of imprecise image feature extraction and an unknown number of initial superpixels and can significantly improve the classification accuracy of HSIs.