Modern Trends in Hyperspectral Image Analysis: A Review <s> B. SPECTRAL RESOLUTION <s> For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. Introductory Digital Image Processing: A Remote Sensing Perspective focuses on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Pearson Series Geographic Information Science. Now in full color, the Fourth Edition provides up-to-date information on analytical methods used to analyze digital remote sensing data. Each chapter contains a substantive reference list that can be used by students and scientists as a starting place for their digital image processing project or research. A new appendix provides sources of imagery and other geospatial information. <s> BIB001 </s> Modern Trends in Hyperspectral Image Analysis: A Review <s> B. SPECTRAL RESOLUTION <s> However, permission to reprint/republish this material for advertising or promotional purposes or for creating other new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from David Landgrebe and the World Scientific Publishing Company. This work was originally prepared for and appears in Informat ion Process ing for Remote Sens ing , edited by C. H. Chen, published by the World Scientific Publishing Co., Inc., 1060 Main Street, River Edge, NJ 07661, USA (Spring, 1999). In addition to general works such as this one, this book contains sections on Pattern Recognition, SAR Image Processing and Segmentation, Parameter Extraction, Neural Network and Fuzzy Logic Methods, Change Detection, Knowledge-based Methods and Data Fusion, Image Processing Algorithms including wavelet analysis techniques, Image Compression, and Discrimination of Buried Objects. Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data David Landgrebe School of Electrical & Computer Engineering Purdue University, West Lafayette, IN 47907-1285 Voice: 765-494-3486 Fax: 765-494-3358 landgreb@ecn.purdue.edu <s> BIB002
Spectral resolution can be defined as the number of spectral bands and range of electromagnetic spectrum measured by the sensor. An imaging sensor might respond to a large frequency range but still have a low spectral resolution if it acquires a small number of spectral bands. On the contrary, if a sensor is sensitive to small frequency range but captures large number of spectral bands has high spectral resolution, due to its ability to distinguish between scene elements having close or similar spectral signatures BIB001 . Multispectral images have a low spectral resolution, thus unable to resolve finer spectral signatures present in the scene. HSI sensors acquire images in numerous contiguous and extremely narrow spectral bands in mid infrared, near infrared and visible segments of electromagnetic spectrum. This type of advance imaging system shows tremendous potential for material identification on the basis of their unique spectral signatures BIB002 . Spectrum of a single pixel in a hyperspectral image can give considerably more information about the surface of the material than a normal image.