Advances in Hyperspectral Image Processing Techniques

Authoritative and comprehensive resource covering recent hyperspectral imaging techniques from theory to applications

Advances in Hyperspectral Image Processing Techniques is derived from recent developments of hyperspectral imaging (HSI) techniques along with new applications in the field, covering many new ideas that have been explored and have led to various new directions in the past few years.

The work gathers an array of disparate research into one resource and explores its numerous applications across a wide variety of disciplinary areas. In particular, it includes an introductory chapter on fundamentals of HSI and a chapter on extensive use of HSI techniques in satellite on-orbit and on-board processing to aid readers involved in these specific fields.

The book’s content is based on the expertise of invited scholars and is categorized into six parts. Part I provides general theory. Part II presents various Band Selection techniques for Hyperspectral Images. Part III reviews recent developments on Compressive Sensing for Hyperspectral Imaging. Part IV includes Fusion of Hyperspectral Images. Part V covers Hyperspectral Data Unmixing. Part VI offers different views on Hyperspectral Image Classification.

Specific sample topics covered in Advances in Hyperspectral Image Processing Techniques include:

• Two fundamental principles of hyperspectral imaging
• Constrained band selection for hyperspectral imaging and class information-based band selection for hyperspectral image classification
• Restricted entropy and spectrum properties for hyperspectral imaging and endmember finding in compressively sensed band domain
• Hyperspectral and LIDAR data fusion, fusion of band selection methods for hyperspectral imaging, and fusion using multi-dimensional information
• Advances in spectral unmixing of hyperspectral data and fully constrained least squares linear spectral mixture analysis
• Sparse representation-based hyperspectral image classification; collaborative hyperspectral image classification; class-feature weighted hyperspectral image classification; target detection approach to hyperspectral image classification

With many applications beyond traditional remote sensing, ranging from defense and intelligence, to agriculture, to forestry, to environmental monitoring, to food safety and inspection, to medical imaging, Advances in Hyperspectral Image Processing Techniques is an essential resource on the topic for industry professionals, researchers, academics, and graduate students working in the field.

Chein-I Chang, PhD, is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County (UMBC). He is a Life Fellow of IEEE and a Fellow of SPIE. He is an Associate Editor of Remote Sensing and IEEE Transaction on Geoscience and Remote Sensing. Dr. Chang has authored four books, edited two books, and co-edited one book.