Understanding Hyperspectral Image and Signal Processing
1st Edition
Will be published approx. 2030
Book
Hardback
512 pages
978-1-118-55051-9 (ISBN)
Description
One of the first texts to focus on investigating, designing and implementing algorithms and computer programs as an introduction to the rapidly evolving field of hyperspectral image and signal processing
Covering a range of applications, the authors provide a tutorial on hyperspectral image analysis, focusing on the mathematical, physical, and algorithmic models necessary to devise programs that can extract the useful information that is present in measured hyperspectral data. The amount of data produced by a hyperspectral imaging device can be enormous so care and advanced processing steps must be taken to efficiently and effectively extract information. The reader will learn about these processing steps. The authors take the readers through the topic step-by-step; from the physics foundations of the acquisition process, to the particular algorithms and families of processing tools for classification, feature selection/extraction, visualization, unmixing and classification. Homework problems are provided whereby some problems are mathematical in nature whereas others involve writing brief computer programs.
Describes the science and hardware technology underlying hyperspectral image analysis.
Focuses on the mathematical and algorithmic concepts for processing hyperspectral data.
Teaches readers the conceptual basis of how the hundreds of bands in spectral pixels can be used to gather information about the materials and objects that are present in the field of view (or scene) of a hyperspectral camera.
Outlines how to write programs that can find things that are smaller than a single pixel, and in turn details how to write programs that can describe and classify components of a scene.
Shows how programs can use spatial information together with spectral information to produce more accurate automated analyses of images.
Illustrates methods with a number of examples from across several applications areas, such as estimating the extent of an oil spill, detecting toxic gases around industrial plants or for homeland security, imaging human tissue to aid medical diagnosis.
Includes companion website hosted by the authors offering publicly available hyperspectral images and sample programs for processing, as well as Matlab code.
Covering a range of applications, the authors provide a tutorial on hyperspectral image analysis, focusing on the mathematical, physical, and algorithmic models necessary to devise programs that can extract the useful information that is present in measured hyperspectral data. The amount of data produced by a hyperspectral imaging device can be enormous so care and advanced processing steps must be taken to efficiently and effectively extract information. The reader will learn about these processing steps. The authors take the readers through the topic step-by-step; from the physics foundations of the acquisition process, to the particular algorithms and families of processing tools for classification, feature selection/extraction, visualization, unmixing and classification. Homework problems are provided whereby some problems are mathematical in nature whereas others involve writing brief computer programs.
Describes the science and hardware technology underlying hyperspectral image analysis.
Focuses on the mathematical and algorithmic concepts for processing hyperspectral data.
Teaches readers the conceptual basis of how the hundreds of bands in spectral pixels can be used to gather information about the materials and objects that are present in the field of view (or scene) of a hyperspectral camera.
Outlines how to write programs that can find things that are smaller than a single pixel, and in turn details how to write programs that can describe and classify components of a scene.
Shows how programs can use spatial information together with spectral information to produce more accurate automated analyses of images.
Illustrates methods with a number of examples from across several applications areas, such as estimating the extent of an oil spill, detecting toxic gases around industrial plants or for homeland security, imaging human tissue to aid medical diagnosis.
Includes companion website hosted by the authors offering publicly available hyperspectral images and sample programs for processing, as well as Matlab code.
More details
Persons
Paul D. Gader, Computer and Information Science and Engineering Department, University of Florida, USA
Professor Gader is a tenured faculty member at the University of Florida, USA. He has investigated a wide variety of algorithmic approaches in the context of solving real-world problems; including medical imaging, vehicle detection and recognition, acoustic signature analysis, and biomedical pattern recognition as well as landmine detection involving hyperspectral image analysis. Prof. Gader is Associate Editor of the journal, IEEE Geoscience and Remote Sensing Letters and has over 245 technical publications in the areas of image and signal processing, applied mathematics, and pattern recognition. He has taught classes which include image processing, artificial neural networks, machine learning and subsurface sensing. Prof. Gader is a senior member of the IEEE
Jocelyn Chanussot, Grenoble-Image-sPeech-Signal-Automatics Lab (GIPSA-Lab), Grenoble Institute of Technology, France
Jocelyn Chanussot is currently Professor of Signal and Image Processing at the Grenoble Institute of Technology, France. He is with the Engineering School of Energy, Water and Environment where he gives lectures in remote sensing, digital image and signal processing, and mathematics. Prof. Chanussot conducts his research with the Department of Signals and Images of the GIPSA-Lab where his research interests include multicomponent image processing, nonlinear filtering, statistical modeling and data fusion in remote sensing. He is Editor-in-Chief of the IEEE Journal of Selected Topics in Earth Observations and Remote Sensing; and was the general chair of the first Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), and now in its 4th year has served as the program chair for each subsequent event which has become the leading dedicated conference in this area. Prof. Chanussot is an IEEE Fellow.
Professor Gader is a tenured faculty member at the University of Florida, USA. He has investigated a wide variety of algorithmic approaches in the context of solving real-world problems; including medical imaging, vehicle detection and recognition, acoustic signature analysis, and biomedical pattern recognition as well as landmine detection involving hyperspectral image analysis. Prof. Gader is Associate Editor of the journal, IEEE Geoscience and Remote Sensing Letters and has over 245 technical publications in the areas of image and signal processing, applied mathematics, and pattern recognition. He has taught classes which include image processing, artificial neural networks, machine learning and subsurface sensing. Prof. Gader is a senior member of the IEEE
Jocelyn Chanussot, Grenoble-Image-sPeech-Signal-Automatics Lab (GIPSA-Lab), Grenoble Institute of Technology, France
Jocelyn Chanussot is currently Professor of Signal and Image Processing at the Grenoble Institute of Technology, France. He is with the Engineering School of Energy, Water and Environment where he gives lectures in remote sensing, digital image and signal processing, and mathematics. Prof. Chanussot conducts his research with the Department of Signals and Images of the GIPSA-Lab where his research interests include multicomponent image processing, nonlinear filtering, statistical modeling and data fusion in remote sensing. He is Editor-in-Chief of the IEEE Journal of Selected Topics in Earth Observations and Remote Sensing; and was the general chair of the first Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), and now in its 4th year has served as the program chair for each subsequent event which has become the leading dedicated conference in this area. Prof. Chanussot is an IEEE Fellow.