AI and Machine Learning for Hyperspectral Image Analysis: A Practical Hands-on Approach Using Python
Format:
Paperback
En stock
0.80 kg
Sí
Nuevo
Amazon
USA
- Hyperspectral imaging has transformed the way we observe and quantify the physical world, capturing hundreds of contiguous spectral bands that reveal subtle chemical, biological, and material signatures invisible to conventional imaging systems. From precision agriculture and mineral exploration to environmental monitoring and defense applications, hyperspectral data enables a richer understanding of complex scenes-but it also introduces significant computational and analytical challenges. AI and Machine Learning for Hyperspectral Image Analysis: A Practical Hands-on Approach Using Python bridges the gap between theory and practice, guiding readers through the principles of hyperspectral data structures, sensor characteristics, calibration workflows, and preprocessing strategies before advancing into modern machine learning and deep learning techniques tailored to high-dimensional spectral cubes. This book emphasizes practical implementation using Python, equipping readers with reproducible workflows for dimensionality reduction, spectral similarity analysis, supervised classification, regression, anomaly detection, unmixing, clustering, segmentation, and change detection. Each chapter builds toward real-world problem solving, culminating in a comprehensive capstone project that integrates the full pipeline-from raw data ingestion to model deployment and evaluation. Designed for students, researchers, and practitioners alike, this text balances mathematical intuition with hands-on coding strategies, enabling readers not only to understand hyperspectral AI methods but to confidently apply them in operational environments.
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