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Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery

Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39759, USA
Author to whom correspondence should be addressed.
Academic Editors: Zhaoliang Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(8), 645;
Received: 5 May 2016 / Revised: 16 July 2016 / Accepted: 2 August 2016 / Published: 6 August 2016
PDF [3356 KB, uploaded 6 August 2016]


In recent years, a novel matching classification strategy inspired by the artificial deoxyribonucleic acid (DNA) technology has been proposed for hyperspectral remote sensing imagery. Such a method can describe brightness and shape information of a spectrum by encoding the spectral curve into a DNA strand, providing a more comprehensive way for spectral similarity comparison. However, it suffers from two problems: data volume is amplified when all of the bands participate in the encoding procedure and full-band comparison degrades the importance of bands carrying key information. In this paper, a new multi-probe based artificial DNA encoding and matching (MADEM) method is proposed. In this method, spectral signatures are first transformed into DNA code words with a spectral feature encoding operation. After that, multiple probes for interesting classes are extracted to represent the specific fragments of DNA strands. During the course of spectral matching, the different probes are compared to obtain the similarity of different types of land covers. By computing the absolute vector distance (AVD) between different probes of an unclassified spectrum and the typical DNA code words from the database, the class property of each pixel is set as the minimum distance class. The main benefit of this strategy is that the risk of redundant bands can be deeply reduced and critical spectral discrepancies can be enlarged. Two hyperspectral image datasets were tested. Comparing with the other classification methods, the overall accuracy can be improved from 1.22% to 10.09% and 1.19% to 15.87%, respectively. Furthermore, the kappa coefficient can be improved from 2.05% to 15.29% and 1.35% to 19.59%, respectively. This demonstrated that the proposed algorithm outperformed other traditional classification methods. View Full-Text
Keywords: classification; DNA encoding; spectral matching; hyperspectral remote sensing classification; DNA encoding; spectral matching; hyperspectral remote sensing

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wu, K.; Zhao, D.; Zhong, Y.; Du, Q. Multi-Probe Based Artificial DNA Encoding and Matching Classifier for Hyperspectral Remote Sensing Imagery. Remote Sens. 2016, 8, 645.

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