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Download PDF by Chein-I Chang: Hyperspectral Data Processing: Algorithm Design and Analysis

By Chein-I Chang

ISBN-10: 0471690562

ISBN-13: 9780471690566

Hyperspectral info Processing: set of rules layout and research is a fruits of the examine carried out within the distant Sensing sign and photograph Processing Laboratory (RSSIPL) on the collage of Maryland, Baltimore County. in particular, it treats hyperspectral photograph processing and hyperspectral sign processing as separate matters in varied different types. so much fabrics coated during this publication can be utilized together with the author’s first ebook, Hyperspectral Imaging: options for Spectral Detection and class, with no a lot overlap.

Many leads to this publication are both new or haven't been explored, provided, or released within the public area. those comprise numerous features of endmember extraction, unsupervised linear spectral combination research, hyperspectral info compression, hyperspectral sign coding and characterization, in addition to purposes to hide goal detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral information Processing comprises 8 significant sections:

half I: offers basics of hyperspectral facts processing
half II: bargains a variety of set of rules designs for endmember extraction
half III: derives thought for supervised linear spectral blend analysis
half IV: designs unsupervised equipment for hyperspectral picture analysis
half V: explores new strategies on hyperspectral info compression
components VI & VII: develops recommendations for hyperspectral sign coding and characterization
half VIII: provides functions in multispectral imaging and magnetic resonance imaging

Hyperspectral information Processing compiles an set of rules compendium with MATLAB codes in an appendix to assist readers enforce many vital algorithms constructed during this ebook and write their very own application codes with out hoping on software program packages.

Hyperspectral info Processing is a important reference if you were concerned with hyperspectral imaging and its ideas, besides people who find themselves new to the subject.

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Extra resources for Hyperspectral Data Processing: Algorithm Design and Analysis

Sample text

11(c, d). 12(c, d) can be described as follows: 1. gov/ the laboratory reflectance data. 2. 12(a) that correspond to the five reflectances obtained in step 1 by the following procedure:  Remove noisy bands from the five reflectance data.  Remove bands with abnormal readings from the spectral library.  In order to measure spectral similarity, there is still a need of removing several bands to account for compatibility. ” The locations of the five minerals are identified by comparing their reflectance spectra against their corresponding lab reflectances in the spectral library.

Second, because of low spectral or spatial resolution most image pixels appear in a mixed form rather than as pure pixels. Consequently, the presence of endmembers is considered to be very rare. From a land use/land cover’s point of view there may be few endmembers that have little impact on image classification. However, from a viewpoint of intelligence endmembers provide crucial and critical information since their existence is unexpected. Specifically, when they appear, only a small population will be present and cannot be identified by prior knowledge.

In terms of the pigeon-hole principle it implies that no two pigeons will be allowed to fly into a single pigeon hole. Here, one remark is noteworthy. When it says that one target is accommodated and specified by one spectral band, it simply means that the target can be best spectrally characterized by this particular band compared to other bands. So, this band is chosen to be its identity like its fingerprint or DNA. If two targets happen to have the same band being used for their best spectral characterization then there is no way to discriminate one from the other.

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Hyperspectral Data Processing: Algorithm Design and Analysis by Chein-I Chang


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