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 was under review for the journal GI but the revision was not accepted.

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

Ansheng Ye, Xiangbing Zhou, Yu Gong, Fang Miao, and Huimin Zhao

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.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Ansheng Ye, Xiangbing Zhou, Yu Gong, Fang Miao, and Huimin Zhao

Status: closed

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
    • AC1: 'Reply on RC1', H. Zhao, 06 Mar 2023
  • RC2: 'Comment on gi-2022-24', Huimin Zhao, 27 Jan 2023
    • AC2: 'Reply on RC2', H. Zhao, 06 Mar 2023
  • CC1: 'Comment on gi-2022-24', Huiling Chen, 07 Feb 2023
    • AC3: 'Reply on CC1', H. Zhao, 06 Mar 2023
  • CC2: 'Comment on gi-2022-24', Huayue Chen, 07 Feb 2023
    • AC4: 'Reply on CC2', H. Zhao, 06 Mar 2023

Status: closed

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
    • AC1: 'Reply on RC1', H. Zhao, 06 Mar 2023
  • RC2: 'Comment on gi-2022-24', Huimin Zhao, 27 Jan 2023
    • AC2: 'Reply on RC2', H. Zhao, 06 Mar 2023
  • CC1: 'Comment on gi-2022-24', Huiling Chen, 07 Feb 2023
    • AC3: 'Reply on CC1', H. Zhao, 06 Mar 2023
  • CC2: 'Comment on gi-2022-24', Huayue Chen, 07 Feb 2023
    • AC4: 'Reply on CC2', H. Zhao, 06 Mar 2023
Ansheng Ye, Xiangbing Zhou, Yu Gong, Fang Miao, and Huimin Zhao
Ansheng Ye, Xiangbing Zhou, Yu Gong, Fang Miao, and Huimin Zhao

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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.