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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods.

Details

Title
Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification
Author
Wang, Nanlan 1 ; Zeng, Xiaoyong 2 ; Duan, Yanjun 3 ; Deng, Bin 4 ; Mo, Yan 4 ; Xie, Zhuojun 4 ; Duan, Puhong 4   VIAFID ORCID Logo 

 School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China; Center for International Education, Philippine Christian University, Manila 1004, Philippines 
 School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China 
 International College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China 
 College of Electrical and Information Engineering, Hunan University, Changsha 410082, China 
First page
8502
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
14248220
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2734747602
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.