Multi-task multi-objective evolutionary network for hyperspectral image classification and pansharpening
•A multi-task multi-objective evolutionary network is proposed.•The framework combines two tasks by effective high-frequency information sharing.•Multi-task learning is modeled as a deep MOEAD to obtain trade-off solutions.•The method provides diverse and sufficient samples for multi-task. Multi-tas...
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| Vydané v: | Information fusion Ročník 108; s. 102383 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.08.2024
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| ISSN: | 1566-2535, 1872-6305 |
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| Abstract | •A multi-task multi-objective evolutionary network is proposed.•The framework combines two tasks by effective high-frequency information sharing.•Multi-task learning is modeled as a deep MOEAD to obtain trade-off solutions.•The method provides diverse and sufficient samples for multi-task.
Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances. To address this challenge, this paper proposes a multi-task multi-objective evolutionary network (DMOEAD) for joint learning of HC and HP. A multi-task sufficiency-and-diversity sampling method is designed to unify the heterogeneity of sample construction between two types of tasks. Two types of task-specific networks are constructed to decompose high-frequency information. Further, a collaborative learning module is designed to dynamically learn complementary high-frequency information from another task in different layers. To be compatible with the optimization direction of two types of tasks, multi-task optimization is realized using a deep multi-objective evolutionary algorithm (DMEO). In the DMEO, the set of parameters of the DMOEAD is regarded as an individual. A deep mutation operator is designed and used for network optimization, which accelerates large-scale network parameter searching. The DMEO can coordinate the differences between multiple tasks and provide a set of Pareto network parameter solutions. Finally, the experimental results demonstrate that the proposed method can significantly enhance the performance of both pansharpening and classification tasks. |
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| AbstractList | •A multi-task multi-objective evolutionary network is proposed.•The framework combines two tasks by effective high-frequency information sharing.•Multi-task learning is modeled as a deep MOEAD to obtain trade-off solutions.•The method provides diverse and sufficient samples for multi-task.
Multi-task learning has commonly been used and performed well at joint visual perception tasks. Hyperspectral pansharpening (HP) and hyperspectral classification (HC) tasks extract high-frequency information to enhance edges and classify samples, offering potential for performance improvements in multi-task learning. However, differences between tasks can make it challenging to balance their performances. To address this challenge, this paper proposes a multi-task multi-objective evolutionary network (DMOEAD) for joint learning of HC and HP. A multi-task sufficiency-and-diversity sampling method is designed to unify the heterogeneity of sample construction between two types of tasks. Two types of task-specific networks are constructed to decompose high-frequency information. Further, a collaborative learning module is designed to dynamically learn complementary high-frequency information from another task in different layers. To be compatible with the optimization direction of two types of tasks, multi-task optimization is realized using a deep multi-objective evolutionary algorithm (DMEO). In the DMEO, the set of parameters of the DMOEAD is regarded as an individual. A deep mutation operator is designed and used for network optimization, which accelerates large-scale network parameter searching. The DMEO can coordinate the differences between multiple tasks and provide a set of Pareto network parameter solutions. Finally, the experimental results demonstrate that the proposed method can significantly enhance the performance of both pansharpening and classification tasks. |
| ArticleNumber | 102383 |
| Author | Shang, Ronghua Feng, Jie Gamba, Paolo Wu, JinJian Zhang, Xiangrong Jiao, Licheng Wu, Xiande |
| Author_xml | – sequence: 1 givenname: Xiande surname: Wu fullname: Wu, Xiande organization: School of Artificial Intelligence, Xidian University, Xi'an 710071, Shaanxi, China – sequence: 2 givenname: Jie orcidid: 0000-0002-8032-7542 surname: Feng fullname: Feng, Jie email: jiefeng0109@163.com organization: School of Artificial Intelligence, Xidian University, Xi'an 710071, Shaanxi, China – sequence: 3 givenname: Ronghua surname: Shang fullname: Shang, Ronghua organization: School of Artificial Intelligence, Xidian University, Xi'an 710071, Shaanxi, China – sequence: 4 givenname: JinJian surname: Wu fullname: Wu, JinJian organization: School of Artificial Intelligence, Xidian University, Xi'an 710071, Shaanxi, China – sequence: 5 givenname: Xiangrong surname: Zhang fullname: Zhang, Xiangrong organization: School of Artificial Intelligence, Xidian University, Xi'an 710071, Shaanxi, China – sequence: 6 givenname: Licheng surname: Jiao fullname: Jiao, Licheng organization: School of Artificial Intelligence, Xidian University, Xi'an 710071, Shaanxi, China – sequence: 7 givenname: Paolo surname: Gamba fullname: Gamba, Paolo organization: Department of Electrical, Biomedical and Computer Engineering, University of Pavia, Pavia 27100, Italy |
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| Keywords | Multi-objective evolution algorithm Hyperspectral pansharpening Multi-task learning Hyperspectral classification |
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