Hyperspectral Remote Sensing for Sustainable Agriculture explores how artificial intelligence and machine learning are transforming Earth Observation and remote sensing. The book focuses on improving the analysis of satellite and hyperspectral imagery through automated, accurate, and scalable methods for environmental and agricultural applications.
It introduces key concepts in Earth Observation and AI, followed by techniques for image preprocessing, classification, feature extraction, and change detection. Application-driven chapters highlight real-world uses in agriculture, forest monitoring, climate studies, disaster management, and environmental assessment. The book combines theoretical understanding with practical workflows, making complex concepts accessible and applicable.
Key Features:
-Introduces AI applications in remote sensing and Earth Observation
-Covers hyperspectral and multispectral image analysis techniques
-Provides practical workflows for classification, feature extraction, and change detection
-Real-world applications in agriculture, climate studies, and environmental monitoring
-Insights into emerging trends such as automated EO pipelines and New Space data
