31 August 2009 Progressive dimensionality reduction for hyperspectral imagery
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Abstract
This paper develops to a new concept, called Progressive Dimensionality Reduction (PDR) which can perform data dimensionality progressive in terms of information preservation. Two procedures can be designed to perform PDR in a forward or backward manner, referred to forward PDR (FPDR) or backward PDR (BPDR) respectively where FPDR starts with a minimum number of spectral-transformed dimensions and increases the spectral-transformed dimension progressively as opposed to BPDR begins with a maximum number of spectral-transformed dimensions and decreases the spectral-transformed dimension progressively. Both procedures are terminated when a stopping rule is satisfied. In order to carry out DR in a progressive manner, DR must be prioritized in accordance with significance of information so that the information after DR can be either increased progressively by FPDR or decreased progressively by BPDR. To accomplish this task, Projection Pursuit (PP)-based DR techniques are further developed where the Projection Index (PI) designed to find a direction of interestingness is used to prioritize directions of Projection Index Components (PICs) so that the DR can be performed by retaining PICs with high priorities via FPDR or BPDR. In the context of PDR, two well-known component analysis techniques, Principal Components Analysis (PCA) and Independent Component Analysis (ICA) can be considered as its special cases when they are used for DR.
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Haleh Safavi, Haleh Safavi, Keng-Hao Liu, Keng-Hao Liu, Chein-I Chang, Chein-I Chang, } "Progressive dimensionality reduction for hyperspectral imagery", Proc. SPIE 7455, Satellite Data Compression, Communication, and Processing V, 745508 (31 August 2009); doi: 10.1117/12.828367; https://doi.org/10.1117/12.828367
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