Preprints
https://doi.org/10.5194/gi-2022-24
https://doi.org/10.5194/gi-2022-24
 
24 Jan 2023
24 Jan 2023
Status: this preprint is currently under review for the journal GI.

Sample labeling and classification method of hyperspectral remote sensing images based on texture features and semi-supervised learning

Ansheng Ye1,2, Xiangbing Zhou3, Yu Gong4, Fang Miao1, and Huimin Zhao4 Ansheng Ye et al.
  • 1Key Lab of Earth Exploration & Information Techniques of Ministry Education, Chengdu University of Technology, Chengdu 610059, China
  • 2School of Computer Science, Chengdu University, Chengdu 610106, China
  • 3School of Information and Engineering, Sichuan Tourism University, Chengdu 610100, China
  • 4College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

Abstract. Hyperspectral images contain abundant spectral and spatial information about the earth's surface, labeling data processing and analysis more difficult, as well as the problem of sample labeling. In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced, and a sample labeling method based on neighborhood information and priority classifier discrimination is presented. Then, a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented. The LBP is employed to extract features of spatial texture information from remote sensing images and enrich the feature information of samples. Then the multivariate logistic regression model is used to select the unlabeled samples with the largest amount of information, and the unlabeled samples with neighborhood information and priority classifier tags are selected to obtain the pseudo-labeled samples after learning. By making full use of the advantages of sparse representation and mixed logistic regression model, a new hyperspectral remote sensing image classification model based on semi-supervised learning is constructed to effectively achieve accurate classification of hyperspectral images. The data of Indian Pines, Salinas scene and Pavia University are selected to verify the validity of the proposed method. The experiment results show that the proposed classification method can obtain higher classification accuracy and show stronger timeliness and generalization ability.

Ansheng Ye et al.

Status: open (until 01 Mar 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gi-2022-24', Anonymous Referee #1, 25 Jan 2023 reply
  • RC2: 'Comment on gi-2022-24', Huimin Zhao, 27 Jan 2023 reply

Ansheng Ye et al.

Ansheng Ye et al.

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Short summary
In this paper, local binary pattern (LBP), sparse representation and mixed logistic regression model are introduced, and a sample labeling method based on neighborhood information and priority classifier discrimination is presented. Then, a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning is implemented.