Effective superpixel sparse representation classification method with multiple features and L0 smoothing for hyperspectral images

In the field of remote sensing, hyperspectral image (HSI) classification is a widely used technique. Recently, there has been an increasing focus on utilizing superpixels for HSI classification. However, noise pixels in superpixels may lead to unsatisfactory classification results. To address this issue, an effective superpixel sparse representation classification method with multiple features and L0 smoothing is proposed. In this method, multifeature extraction utilizes the diversity of HSIs’ spectral–spatial information, band fusion effectively reduces redundant information and noise of HSIs, and L0 smoothing improves superpixel segmentation results by strengthening homogeneous neighborhoods and edges. Meanwhile, simple linear iterative clustering is adopted to acquire superpixels of HSIs. Finally, the majority voting strategy is adopted to determine the final classification result, improving the classification accuracy. To verify the performance of the proposed method, three hyperspectral datasets are selected for experiments. The experimental results show that the proposed method is superior to some famous classification methods.