FULLY UNSUPERVISED HYPERSPECTRAL IMAGE ANALYSIS

Ahmed Mohamed Ahmed Saied El Sheikh A Thesis Submitted to the Faculty of Engineering at Cairo University in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Engineering Physics;

Abstract


(Key Words: Hyperspectral; Virtual dimension; Hyperspectral signal identification by min-imum error; Vertex components analysis; Automatic target generation process; Independent components analysis; Principal components analysis; Self-organizing map; Orthogonal sub-space projection; Fully constrained least squares)

Hyperspectral imaging (HSI) -also called Imaging Spectroscopy- sensors observe hundreds or thousands of contiguous spectral bands as well as spatial locality. A hyperspectral image cube (two spatial dimensions and the third is the wavelength) contains a large amount of informa-tion about the imaged scenario. Thus the automated analysis of such image cubes is an impor-tant asset. This thesis suggested a pre-clustering step, and bases search techniques which have shown improvements of the known algorithms.


Other data

Title FULLY UNSUPERVISED HYPERSPECTRAL IMAGE ANALYSIS
Other Titles تحليل مكتمل بغير إشراف لصور الطيف الفائق
Authors Ahmed Mohamed Ahmed Saied El Sheikh A Thesis Submitted to the Faculty of Engineering at Cairo University in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Engineering Physics
Issue Date 2014

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