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• W3200465006 abstract "A band selection algorithm named space and information comprehensive evaluation model (SICEM) is proposed in this paper, which reconstitutes the hyperspectral imagery by building an optimal subset to replace the original spectrum. SICEM reduces the dimensions while keeping the vital information of an image, and these are accomplished through two phases. Specifically, the improved fast density peaks clustering (I-FDPC) algorithm is employed to pick out the scattered bands in geometric space to generate a candidate set <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>U</mi> </math> at first. Then, we conduct pruning in <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <mi>U</mi> </math> through iterative information analysis until the target set <math xmlns=http://www.w3.org/1998/Math/MathML id=M3> <mi mathvariant=normal>Ω</mi> </math> is built. In this phase, we need to calculate comprehensive information score (CIS) for every member in <math xmlns=http://www.w3.org/1998/Math/MathML id=M4> <mi>U</mi> </math> after assigning weights to the amount of information (AoI) and correlation. In each iteration, the band with highest score is selected into <math xmlns=http://www.w3.org/1998/Math/MathML id=M5> <mi mathvariant=normal>Ω</mi> </math> , and the ones highly related to it will be removed out of <math xmlns=http://www.w3.org/1998/Math/MathML id=M6> <mi>U</mi> </math> via a threshold. Compared with the four state-of-the-art unsupervised algorithms on real-world HSI datasets (IndianP and PaviaU), we find that SICEM has strong ability to form an optimal reduced-dimension combination with low correlation and rich information and it performs well in discrete band distribution, accuracy, consistency, and stability." @default.
• W3200465006 created "2021-09-27" @default.
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• W3200465006 date "2021-09-18" @default.
• W3200465006 modified "2023-10-16" @default.
• W3200465006 title "SICEM: A Generation Approach of Band Combination for Hyperspectral Imagery Reconstitution Based on Space and Information Analyses" @default.
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• W3200465006 doi "https://doi.org/10.1155/2021/8178495" @default.
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