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Springer Hyperspectral Imaging: Techniques for Spectral Detection and Classification

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Description

Hyperspectral Imaging: Techniques for Spectral Detection and Classification by Springer is a comprehensive guide developed from decades of research at the Remote Sensing Signal and Image Processing Laboratory at the University of Maryland, Baltimore County. This foundational text applies statistical signal processing to hyperspectral imaging, introducing non-literal spectral techniques for subpixel detection and mixed pixel classification. As the first book of its kind on this subject, it functions as a practical recipe book offering proven methods for hyperspectral data exploitation. The work bridges advanced signal processing theory with real-world remote sensing applications, making it an essential resource for researchers, engineers, and practitioners working in spectral detection, image classification, and remote sensing technology.

Key Features

Hyperspectral Imaging explores statistical signal processing applications tailored specifically for hyperspectral imaging analysis and interpretation.

The book presents non-literal spectral techniques developed at RSSIPL for accurate subpixel detection in complex imaging scenarios.

Mixed pixel classification methods in this Springer text address the challenge of identifying multiple materials within single image pixels.

Practical techniques offered throughout function as a recipe book for professionals seeking proven hyperspectral data exploitation strategies.

Research-backed approaches stem from extensive work conducted at the University of Maryland, Baltimore County's Remote Sensing laboratory.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
31 July 2003
Listed Since
07 February 2007

Barcode

No barcode data available

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