文献类型：期刊文献

英文题名：Effective superpixel sparse representation classification method with multiple features and L₀ smoothing for hyperspectral images

作者：Lin, Huixian[1];Du, Hong[1];Zhang, Xiaoguang[2]

机构：[1]Guangdong Ocean Univ, Fac Math & Comp Sci, Zhanjiang, Peoples R China;[2]Guangdong Ocean Univ Yangjiang Campus, Fac Math & Comp Sci, Yangjiang, Peoples R China

年份：2023

卷号：17

期号：4

外文期刊名：JOURNAL OF APPLIED REMOTE SENSING

收录：SCI-EXPANDED(收录号：WOS:001134904400012)、、WOS

基金：This work was supported by Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515010022).

语种：英文

外文关键词：hyperspectral images; superpixels; multiple features; sparse representation; L (0) smoothing

外文摘要：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 L-0 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 L-0 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.