Hao Li;Dezhong Li;Maoguo Gong;Jianzhao Li;A. K. Qin;Lining Xing;Fei Xie
{"title":"基于偏好的多目标进化多任务优化的稀疏高光谱解混技术","authors":"Hao Li;Dezhong Li;Maoguo Gong;Jianzhao Li;A. K. Qin;Lining Xing;Fei Xie","doi":"10.1109/TETCI.2024.3359070","DOIUrl":null,"url":null,"abstract":"The traditional sparse unmixing methods based on multiobjective evolutionary algorithms (MOEAs) only deal with a single mixed pixel, without considering the spatial structure relationship between different mixed pixels. In addition, these methods suffer from the curse of dimensionality caused by the large number of pixels in hyperspectral image and spectra in library. In this paper, an evolutionary multitasking unmixing based on weakly nondominated sorting (EMTU-WNS) algorithm is proposed to alleviate these existing issues. First, a hyperspectral image is classified into multiple homogeneous regions, and the unmixing of pixels in the same region is constructed as a multiobjective optimization task. Then all the tasks are optimized simultaneously by using a population in the design of genetic transfer of intra-task and inter-task. In comparison with the original unmixing task with all pixels, these tasks in multiple homogeneous regions are relatively simple in term of dimensionality. Furthermore, it is inefficient for individuals to explore the whole search space. Therefore sparsity-constrained genetic operators are designed to evolve individuals towards the preference sparsity region. Finally, a preference-based weakly nondominated sorting is proposed to increase the number of nondominated solutions and maintain the diversity. The experimental results on three hyperspectral data sets demonstrate the effectiveness of EMTU-WNS with better convergence characteristics and unmixing accuracy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sparse Hyperspectral Unmixing With Preference-Based Evolutionary Multiobjective Multitasking Optimization\",\"authors\":\"Hao Li;Dezhong Li;Maoguo Gong;Jianzhao Li;A. K. Qin;Lining Xing;Fei Xie\",\"doi\":\"10.1109/TETCI.2024.3359070\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The traditional sparse unmixing methods based on multiobjective evolutionary algorithms (MOEAs) only deal with a single mixed pixel, without considering the spatial structure relationship between different mixed pixels. In addition, these methods suffer from the curse of dimensionality caused by the large number of pixels in hyperspectral image and spectra in library. In this paper, an evolutionary multitasking unmixing based on weakly nondominated sorting (EMTU-WNS) algorithm is proposed to alleviate these existing issues. First, a hyperspectral image is classified into multiple homogeneous regions, and the unmixing of pixels in the same region is constructed as a multiobjective optimization task. Then all the tasks are optimized simultaneously by using a population in the design of genetic transfer of intra-task and inter-task. In comparison with the original unmixing task with all pixels, these tasks in multiple homogeneous regions are relatively simple in term of dimensionality. Furthermore, it is inefficient for individuals to explore the whole search space. Therefore sparsity-constrained genetic operators are designed to evolve individuals towards the preference sparsity region. Finally, a preference-based weakly nondominated sorting is proposed to increase the number of nondominated solutions and maintain the diversity. The experimental results on three hyperspectral data sets demonstrate the effectiveness of EMTU-WNS with better convergence characteristics and unmixing accuracy.\",\"PeriodicalId\":13135,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-02-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10432946/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10432946/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Sparse Hyperspectral Unmixing With Preference-Based Evolutionary Multiobjective Multitasking Optimization
The traditional sparse unmixing methods based on multiobjective evolutionary algorithms (MOEAs) only deal with a single mixed pixel, without considering the spatial structure relationship between different mixed pixels. In addition, these methods suffer from the curse of dimensionality caused by the large number of pixels in hyperspectral image and spectra in library. In this paper, an evolutionary multitasking unmixing based on weakly nondominated sorting (EMTU-WNS) algorithm is proposed to alleviate these existing issues. First, a hyperspectral image is classified into multiple homogeneous regions, and the unmixing of pixels in the same region is constructed as a multiobjective optimization task. Then all the tasks are optimized simultaneously by using a population in the design of genetic transfer of intra-task and inter-task. In comparison with the original unmixing task with all pixels, these tasks in multiple homogeneous regions are relatively simple in term of dimensionality. Furthermore, it is inefficient for individuals to explore the whole search space. Therefore sparsity-constrained genetic operators are designed to evolve individuals towards the preference sparsity region. Finally, a preference-based weakly nondominated sorting is proposed to increase the number of nondominated solutions and maintain the diversity. The experimental results on three hyperspectral data sets demonstrate the effectiveness of EMTU-WNS with better convergence characteristics and unmixing accuracy.
期刊介绍:
The IEEE Transactions on Emerging Topics in Computational Intelligence (TETCI) publishes original articles on emerging aspects of computational intelligence, including theory, applications, and surveys.
TETCI is an electronics only publication. TETCI publishes six issues per year.
Authors are encouraged to submit manuscripts in any emerging topic in computational intelligence, especially nature-inspired computing topics not covered by other IEEE Computational Intelligence Society journals. A few such illustrative examples are glial cell networks, computational neuroscience, Brain Computer Interface, ambient intelligence, non-fuzzy computing with words, artificial life, cultural learning, artificial endocrine networks, social reasoning, artificial hormone networks, computational intelligence for the IoT and Smart-X technologies.