Computational Intelligence in Remote Sensing (Hardcover)

Yue Wu

ISBN 10: 3725857555 / ISBN 13: 9783725857555

Published by Mdpi AG, 2025

About this Item

Hardcover. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9783725857555

Bibliographic Details

• Title: Computational Intelligence in Remote Sensing (Hardcover)
• Publisher: Mdpi AG
• Publication Date: 2025
• Language: English
• ISBN 10: 3725857555
• ISBN 13: 9783725857555
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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.