Hyperspectral Image Processing Methods

Hyperspectral image processing extracts, stores and manipulates both spatial and spectral information contained in hyperspectral images across the visible and near-infrared portion of the electromagnetic spectrum. The demand for new hyperspectral image processing tools and techniques more appropriate for near sensing in laboratories or fields of various science and engineering communities has been increasing in recent years. In addition to the hyperspectral image processing algorithms developed for remote sensing applications, chemometrics and multivariate statistical data analysis techniques and their preprocessing techniques have been applied to process hyperspectral images. Hyperspectral image processing workflows are fundamentally different from the conventional color image processing workflows although both data types are multidimensional and multivariate. A typical hyperspectral image processing workflow for near-sensing applications includes normalization, correction, dimensionality reduction, spectral library building, and data processing. In this book chapter, recent advances in hyperspectral image processing algorithms and workflows for hyperspectral image processing are discussed. The main topics are image acquisition, calibration, spectral and spatial preprocessing, scatter correction, binning, and feature extraction and selection.

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