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Publication Type : Journal Article
Publisher : Journal of Applied Remote Sensing
Source : Journal of Applied Remote Sensing , Volume 14, Issue 3, p.036507 (IF: 1.370, CiteScore: 2.6, Q2- 69 percentile) (2020)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2020
Abstract : High-dimensional features often cause computational complexity and dimensionality curse. Feature selection and feature extraction are the two mainstream methods for dimensionality reduction. Feature selection but not feature extraction can preserve the critical information and maintain the physical meaning simultaneously. Herein, we proposed a dimensionality reduction method based on rough set theory (DRM-RST) for feature selection. We defined the hyperspectral image as a decision system, extracted the features as decision attributes, and selected the effective features based on information entropy. We used the Washington D.C. Mall dataset and New York dataset to evaluate the performance of DRM-RST on dimensionality reduction. Compared with full band classification, 184 or 185 redundant bands were removed in DRM-RST, respectively. DRM-RST achieved similar accuracy (overall accuracy >94%) by SVM classifier and reduced computing time by about 85%. We further compared the dimensionality reduction efficiency of DRM-RST against other popular methods, including ReliefF, Sequential Backward Elimination (SBE) and Information Gain (IG). The Producer’s accuracy (PA) and User’s accuracy (UA) of DRM-RST was greater than that of ReliefF and IG. DRM-RST showed greater stability of accuracy than SBE in dimensionality reduction when using for different datasets. Collectively, this study provides a new method for dimensionality reduction that can reduce computational complexity and alleviate dimensionality curse.
Cite this Research Publication : Chippy Jayaprakash, Damodaran, B. Bhushan, Sowmya V., and Dr. Soman K. P., “Dimensionality Reduction Methods for Hyperspectral Image Classification”, Journal of Applied Remote Sensing , vol. 14, no. 3, pp. 036507 (IF: 1.370, CiteScore: 2.6, Q2- 69 percentile), 2020.