An associative and nonparametric digital information processing technique for application in real-time, on-board pattern recognition

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dc.contributor.advisorRhyne, V. T.
dc.creatorMalek-Shahmirzadi, Homayoun
dc.date.accessioned2020-08-21T21:35:02Z
dc.date.available2020-08-21T21:35:02Z
dc.date.issued1977
dc.descriptionVita.en
dc.description.abstractAn inherent characteristic in a synoptic data acquisition system, such as Landsat, is a voluminous data rate. A proposed method of data reduction or data compression is to perform real-time discriminant analysis on the acquired data aboard the data collecting platform. Such a data analysis system, however, requires both high computational speed and minimal computational hardware. The pattern recognition algorithms that are currently applied to the remote sensing data do not collectively meet those special requirements. As a feasible solution to the problem of on-board classification of the Landsat MSS data, the Real-time, Associative Pattern Identification (RAPID) technique is developed and described herein. The RAPID technique is formulated based on the criteria of minimizing (1) probability of misclassification, (2) computational time and (3) computational hardware. In part, these constraints are satisfied with a nonparametric approach and assumption of statistical independence of the components of the feature vector. Further optimization of the classification technique is achieved by design of a special purpose digital processor which is configured around the associative information storage and retrieval concept.en
dc.format.digitalOriginreformatted digitalen
dc.format.extentxi, 136 leavesen
dc.format.mediumelectronicen
dc.format.mimetypeapplication/pdf
dc.identifier.oclc3586375
dc.identifier.urihttps://hdl.handle.net/1969.1/DISSERTATIONS-368416
dc.language.isoeng
dc.publisher.digitalTexas A&M University. Libraries
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectMultispectral imagingen
dc.subjectPattern recognition systemsen
dc.subjectRemote sensingen
dc.subjectMajor electrical engineeringen
dc.subject.classification1977 Dissertation M245
dc.subject.lcshPattern recognition systemsen
dc.subject.lcshMultispectral imagingen
dc.subject.lcshRemote sensingen
dc.titleAn associative and nonparametric digital information processing technique for application in real-time, on-board pattern recognitionen
dc.typeThesisen
dc.type.genredissertationsen
dc.type.materialtexten
thesis.degree.grantorTexas A&M Universityen
thesis.degree.nameDoctor of Philosophyen

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