Computational Intelligence in Remote Sensing
With the advancement of Earth observation techniques, vast amounts of high-resolution remote sensing data are continually captured, proving instrumental in fields such as geography, environmental monitoring, disaster management, and more. However, challenges such as data volume, complex structures,...
সংরক্ষণ করুন:
| বিন্যাস: | Online |
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| ভাষা: | ইংরেজি |
| প্রকাশিত: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | ONIX_20240514_9783725804139_331 |
| ট্যাগগুলো: |
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| _version_ | 1869528590104657920 |
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| collection | Directory of Open Access Books |
| description | With the advancement of Earth observation techniques, vast amounts of high-resolution remote sensing data are continually captured, proving instrumental in fields such as geography, environmental monitoring, disaster management, and more. However, challenges such as data volume, complex structures, limited labeled samples, and non-convex optimization persist in processing and analyzing remote sensing data. Computational intelligence techniques, inspired by biological intelligence systems, offer potential solutions to these challenges. Computational intelligence (CI) is the theory, design, and application of biologically and linguistically motivated computational paradigms. Traditionally centered around neural networks, fuzzy systems, and evolutionary computation, CI has expanded to include various nature-inspired computing paradigms. These paradigms encompass ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a vital role in developing intelligent systems, including games and cognitive developmental systems. Recent years have seen a surge in deep learning research, with deep convolutional neural networks becoming a core method in artificial intelligence. Many successful AI systems today are based on CI, and it is anticipated that CI will provide effective solutions to challenges in remote sensing in the future. |
| format | Online |
| id | doab-20.500.12854ir-137735 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1377352024-05-14T14:17:34Z Computational Intelligence in Remote Sensing Wu, Yue Qin, Kai Gong, Maoguo Miao, Qiguang artificial intelligence machine learning deep learning neural networks computer vision evolutionary computation fuzzy logic and systems thema EDItEUR::P Mathematics and Science::PN Chemistry::PNK Inorganic chemistry With the advancement of Earth observation techniques, vast amounts of high-resolution remote sensing data are continually captured, proving instrumental in fields such as geography, environmental monitoring, disaster management, and more. However, challenges such as data volume, complex structures, limited labeled samples, and non-convex optimization persist in processing and analyzing remote sensing data. Computational intelligence techniques, inspired by biological intelligence systems, offer potential solutions to these challenges. Computational intelligence (CI) is the theory, design, and application of biologically and linguistically motivated computational paradigms. Traditionally centered around neural networks, fuzzy systems, and evolutionary computation, CI has expanded to include various nature-inspired computing paradigms. These paradigms encompass ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a vital role in developing intelligent systems, including games and cognitive developmental systems. Recent years have seen a surge in deep learning research, with deep convolutional neural networks becoming a core method in artificial intelligence. Many successful AI systems today are based on CI, and it is anticipated that CI will provide effective solutions to challenges in remote sensing in the future. 2024-05-14T14:17:30Z 2024-05-14T14:17:30Z 2024 book ONIX_20240514_9783725804139_331 9783725804139 9783725804146 https://directory.doabooks.org/handle/20.500.12854/137735 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/topic/8965 https://mdpi.com/books/pdfview/topic/8965 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0414-6 10.3390/books978-3-7258-0414-6 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725804139 9783725804146 404 open access |
| spellingShingle | artificial intelligence machine learning deep learning neural networks computer vision evolutionary computation fuzzy logic and systems thema EDItEUR::P Mathematics and Science::PN Chemistry::PNK Inorganic chemistry Computational Intelligence in Remote Sensing |
| title | Computational Intelligence in Remote Sensing |
| title_full | Computational Intelligence in Remote Sensing |
| title_fullStr | Computational Intelligence in Remote Sensing |
| title_full_unstemmed | Computational Intelligence in Remote Sensing |
| title_short | Computational Intelligence in Remote Sensing |
| title_sort | computational intelligence in remote sensing |
| topic | artificial intelligence machine learning deep learning neural networks computer vision evolutionary computation fuzzy logic and systems thema EDItEUR::P Mathematics and Science::PN Chemistry::PNK Inorganic chemistry |
| topic_facet | artificial intelligence machine learning deep learning neural networks computer vision evolutionary computation fuzzy logic and systems thema EDItEUR::P Mathematics and Science::PN Chemistry::PNK Inorganic chemistry |
| url | ONIX_20240514_9783725804139_331 |