Preface
Over the last fifty years, a large number of spaceborne and airborne sensors
have been employed to gather information regarding the earth's surface and
environment. As sensor technology continues to advance, remote sensing data
with improved temporal, spectral, and spatial resolution is becoming more
readily available. This widespread availability of enormous amounts of data
has necessitated the development of efficient data processing techniques for
a wide variety of applications. In particular, great strides have been made in the
development of digital image processing techniques for remote sensing data.
The goal has been efficient handling of vast amounts of data, fusion of data
from diverse sensors, classification for image interpretation, and development
of user-friendly products that allow rich visualization.
This book presents some new algorithms that have been developed for highdimensional datasets, such as multispectral and hyperspectral imagery. The
contents of the book are based primarily on research carried out by some
members and alumni of the Sensor Fusion Laboratory at Syracuse University.
Early chapters that provide an overview of multispectral and hyperspectral
sensing, and digital image processing have been prepared by other leading
experts in the field. The intent of this book is to present the material at a level
suitable for a diverse audience ranging from beginners to advanced remote
sensing researchers and practitioners. This book can be used as a text or
a reference in courses on image analysis and remote sensing. It can also be
used as a reference book by remote sensing researchers, scientists and users,
as well as by resource managers and planners.
We would like to thank all the authors for their enthusiasm and active
cooperation during the course of this project. The research conducted at Syracuse University was supported by NASA under grant NAGS-11227. We thank
Syracuse University for assistance in securing this grant. We also thank the
Indian Institute of Technology, Roorkee for granting a postdoctoral leave that
enabled Manoj K. Arora to participate in this endeavor. We are grateful to several organizations for providing and allowing the use of remote sensing data
in the illustrative examples presented in this book. They include the Laboratory for Applications of Remote Sensing - Purdue University (AVIRIS data),
USGS (Landsat ETM+ data), Eastman Kodak (IRS PAN, Radarsat SAR, and
HyMap data as well as digital aerial photographs), NASA (IKONOS data) and
DIRS laboratory of the Center for Imaging Science at Rochester Institute of
Technology.
April 2004
Pramod K. Varshney
Manoj K. Arora
Over the last fifty years, a large number of spaceborne and airborne sensors
have been employed to gather information regarding the earth's surface and
environment. As sensor technology continues to advance, remote sensing data
with improved temporal, spectral, and spatial resolution is becoming more
readily available. This widespread availability of enormous amounts of data
has necessitated the development of efficient data processing techniques for
a wide variety of applications. In particular, great strides have been made in the
development of digital image processing techniques for remote sensing data.
The goal has been efficient handling of vast amounts of data, fusion of data
from diverse sensors, classification for image interpretation, and development
of user-friendly products that allow rich visualization.
This book presents some new algorithms that have been developed for highdimensional datasets, such as multispectral and hyperspectral imagery. The
contents of the book are based primarily on research carried out by some
members and alumni of the Sensor Fusion Laboratory at Syracuse University.
Early chapters that provide an overview of multispectral and hyperspectral
sensing, and digital image processing have been prepared by other leading
experts in the field. The intent of this book is to present the material at a level
suitable for a diverse audience ranging from beginners to advanced remote
sensing researchers and practitioners. This book can be used as a text or
a reference in courses on image analysis and remote sensing. It can also be
used as a reference book by remote sensing researchers, scientists and users,
as well as by resource managers and planners.
We would like to thank all the authors for their enthusiasm and active
cooperation during the course of this project. The research conducted at Syracuse University was supported by NASA under grant NAGS-11227. We thank
Syracuse University for assistance in securing this grant. We also thank the
Indian Institute of Technology, Roorkee for granting a postdoctoral leave that
enabled Manoj K. Arora to participate in this endeavor. We are grateful to several organizations for providing and allowing the use of remote sensing data
in the illustrative examples presented in this book. They include the Laboratory for Applications of Remote Sensing - Purdue University (AVIRIS data),
USGS (Landsat ETM+ data), Eastman Kodak (IRS PAN, Radarsat SAR, and
HyMap data as well as digital aerial photographs), NASA (IKONOS data) and
DIRS laboratory of the Center for Imaging Science at Rochester Institute of
Technology.
April 2004
Pramod K. Varshney
Manoj K. Arora