Sample labeling and classification method of hyperspectral remote sensing images based on texture features and semi-supervised learning
- 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
- 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)
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RC1: 'Comment on gi-2022-24', Anonymous Referee #1, 25 Jan 2023
reply
 The results look encouraging and motivating. But some contents need be revised in order to meet the requirements of publish.
(1)The abstract should be improved. Your point is your own work that should be further highlighted.
(2)The parameters in expressions are given and explained.
(3) The method in the context of the proposed work should be written in detail.
(4) The values of parameters could be a complicated problem itself, how the authors give the values of parameters in the used methods.
(5) The literature review is poor in this paper. You must review all significant similar works that have been done. I hope that the authors can add some new references in order to improve the reviews and the connection with the literatures.
(6) The main contributions of this paper should be further summarized and clearly demonstrated. This reviewer suggests the authors exactly mention what is new compared with existing.
(7) The conclusion and motivation of the work should be added in a clearer way.
(8) There are some grammatical errors seen in the paper. Check carefully for a few clerical errors and formatting issues.
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RC2: 'Comment on gi-2022-24', Huimin Zhao, 27 Jan 2023
reply
The authors proposed a hyperspectral remote sensing image classification method based on texture features and semi-supervised learning. 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. However, it requires further improvements.
(1) In the abstract section, I would suggest that the author should provide to the point and quantitative advantages of the proposed method.
(2) In the introduction, the authors should clearly indicate the contributions and innovations of this paper.
(3) All acronyms and variables in equations must be defined in the article.
(4) In Section 3.2, how to realize the sample labeling by using neighborhood information and priority classifier?
(5) Figure 2 and Figure3 are not clear, please provide some clear figures.
(6) Why did you use the selected evaluation criteria? What are their advantages?
(7) There are some grammatical mistakes and typo errors. Please proof read from native speaker.
(8) Please add what the next work of this article is.
(9) Some new references should be added to improve the reviews the literatures.
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Ansheng Ye et al.
Ansheng Ye et al.
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