Classification Of Hyperspectral Remote Sensing Images
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Spectral-Spatial Classification of Hyperspectral Remote Sensing Images
Author: Jon Atli Benediktsson
language: en
Publisher: Artech House
Release Date: 2015-09-01
This comprehensive new resource brings you up to date on recent developments in the classification of hyperspectral images using both spectral and spatial information, including advanced statistical approaches and methods. The inclusion of spatial information to traditional approaches for hyperspectral classification has been one of the most active and relevant innovative lines of research in remote sensing during recent years. This book gives you insight into several important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. This book also shows you how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide.
Hyperspectral Remote Sensing
Author: Michael Theodore Eismann
language: en
Publisher: SPIE-International Society for Optical Engineering
Release Date: 2012
Hyperspectral remote sensing is an emerging, multidisciplinary field with diverse applications that builds on the principles of material spectroscopy, radiative transfer, imaging spectrometry, and hyperspectral data processing. While there are many resources that suitably cover these areas individually and focus on specific aspects of the hyperspectral remote sensing field, this book provides a holistic treatment that captures its multidisciplinary nature. The content is oriented toward the physical principles of hyperspectral remote sensing as opposed to applications of hyperspectral technology. Readers can expect to finish the book armed with the required knowledge to understand the immense literature available in this technology area and apply their knowledge to the understanding of material spectral properties, the design of hyperspectral systems, the analysis of hyperspectral imagery, and the application of the technology to specific problems.
Classification of Hyperspectral Remote Sensing Images
Recent advances in hyperspectral remote sensor technology allow the simultaneous acquisition of hundreds of spectral wavelengths for each image pixel. Hyperspectral imaging systems can acquire numerous contiguous spectral bands throughout the electromagnetic spectrum. Therefore, hyperspectral imaging techniques are widely used for many applications, including environmental monitoring, mineralogy, astronomy, surveillance and defense. Nevertheless, the high dimensionality of the pixels, undesirable noise, high spectral redundancy and spectral and spatial variabilities, in conjunction with limited ground truth data, present challenges for the analysis of hyperspectral imagery. The classification technology is currently the predominate method for analyzing hyperspectral images and has received much attention. Over the past decades, numerous pixel-wise classification methods, which only use spectral information, have been proposed to classify remote sensing images. Recent advances in spectral-spatial classification of hyperspectral images are presented in this book. Several techniques are investigated for combining both spatial and spectral information. The book highlights the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validates the proposed methods. Spectral-Spatial Classification of Hyperspectral Remote Sensing Images presents insight into numerous important challenges when performing hyperspectral image classification related to the imbalance between high dimensionality and limited availability of training samples, or the presence of mixed pixels in the data. The book also demonstrates the reader how to integrate spatial and spectral information in order to take advantage of the benefits that both sources of information provide.