Computational Intelligence in Remote Sensing
With the rapid advancement of Earth observation technologies, the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains, including environmental monitoring, land use classification, disaster management, and defense. However, the processing and interpret...
محفوظ في:
| التنسيق: | Online |
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| اللغة: | الإنجليزية |
| منشور في: |
MDPI - Multidisciplinary Digital Publishing Institute
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | ONIX_20260416T142754_9783725857555_22 |
| الوسوم: |
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| _version_ | 1869522754487713792 |
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| collection | Directory of Open Access Books |
| description | With the rapid advancement of Earth observation technologies, the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains, including environmental monitoring, land use classification, disaster management, and defense. However, the processing and interpretation of such data are increasingly challenged by large volumes, complex spatial–spectral structures, and limited labeled samples. Computational Intelligence (CI), inspired by natural and biological systems, offers promising strategies to address these complexities through adaptive learning, feature extraction, and decision-making. This reprint explores the latest CI-driven methodologies applied to remote sensing tasks, as reflected in recent advancements such as hybrid retrieval systems, lightweight segmentation networks, few-shot classification models, semantic-enhanced image captioning, and dual-domain transformers for change detection. It highlights contributions from contemporary studies that tackle practical challenges in RS, including pan-sharpening, road extraction, radar imaging, and hyperspectral unmixing. By integrating theory with state-of-the-art applications, the reprint serves as a valuable reference for researchers and professionals working at the intersection of artificial intelligence and Earth observation. |
| format | Online |
| id | doab-20.500.12854ir-174917 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1749172026-04-16T17:21:07Z 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::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries With the rapid advancement of Earth observation technologies, the availability of high-resolution remote sensing (RS) data has expanded across a wide range of domains, including environmental monitoring, land use classification, disaster management, and defense. However, the processing and interpretation of such data are increasingly challenged by large volumes, complex spatial–spectral structures, and limited labeled samples. Computational Intelligence (CI), inspired by natural and biological systems, offers promising strategies to address these complexities through adaptive learning, feature extraction, and decision-making. This reprint explores the latest CI-driven methodologies applied to remote sensing tasks, as reflected in recent advancements such as hybrid retrieval systems, lightweight segmentation networks, few-shot classification models, semantic-enhanced image captioning, and dual-domain transformers for change detection. It highlights contributions from contemporary studies that tackle practical challenges in RS, including pan-sharpening, road extraction, radar imaging, and hyperspectral unmixing. By integrating theory with state-of-the-art applications, the reprint serves as a valuable reference for researchers and professionals working at the intersection of artificial intelligence and Earth observation. 2026-04-16T17:21:01Z 2026-04-16T17:21:01Z 2025 book ONIX_20260416T142754_9783725857555_22 9783725857555 9783725857562 https://directory.doabooks.org/handle/20.500.12854/174917 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/ https://mdpi.com/books/pdfview/topic/11799 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-5756-2 10.3390/books978-3-7258-5756-2 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725857555 9783725857562 234 CH open access |
| spellingShingle | Artificial intelligence Machine learning Deep learning Neural networks Computer vision Evolutionary computation Fuzzy logic and systems thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries 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::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| topic_facet | Artificial intelligence Machine learning Deep learning Neural networks Computer vision Evolutionary computation Fuzzy logic and systems thema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNT Media, entertainment, information and communication industries::KNTX Information technology industries |
| url | ONIX_20260416T142754_9783725857555_22 |