Hyperspectral Image Analysis with Python

Hyperspectral imaging acquires image data across numerous contiguous spectral bands, enabling detailed analysis and material identification beyond conventional imaging techniques. Owing to this capability, hyperspectral image analysis has become increasingly important in applications such as agriculture, environmental monitoring, mineral exploration, urban studies, and defense. Nevertheless, the high dimensionality and complexity of hyperspectral data present significant challenges for beginners.
This book, Hyperspectral Image Analysis with Python: Fundamentals, Processing, and Applications , provides a structured and practice-oriented introduction to hyperspectral image analysis. It is designed to establish a solid foundation in fundamental concepts while emphasizing practical implementation using Python and real hyperspectral datasets.
The book covers hyperspectral data representation, commonly used benchmark datasets, data visualization, and essential preprocessing techniques, including normalization, noise handling, and dimensionality reduction using Principal Component Analysis. It further introduces classical machine learning methods for hyperspectral image classification and discusses standard performance evaluation metrics. The volume concludes with an end-to-end practical case study that integrates all stages of hyperspectral image analysis into a coherent workflow.

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