NDLI: The Modified Clonal Selection Algorithm Applied to the Remote Sensing Image Information Extracting

Content Provider	IEEE Xplore Digital Library
Author	Ling Cheng-xing Zhang Huai-qing Lin Hui
Copyright Year	2009
Abstract	Clonal Selection Algorithm is one of the important artificial intelligence algorithms, which used as a powerful information processing and problem-solving paradigm in both the scientific and engineering fields. However, few studies concern application of CLONALG method in information extracting of satellite image. In this paper, we suggest modified Clonal Selection Algorithm (M-CLONALG) improved by niche technology’s sharing function and memory calculator technique applying to remote sensing (RS) image information extracting. In order to testify the correctness that M-CLONALG improves the stability of searching results, accuracy of the global optimization, we compare with Maximum Likelihood method, Minimum Distance method and normal CLONALG method . Experimental results confirm that M-CLONALG has self-organizing, self-learning ability, and no limitation to the distribution of training samples from the global data, with complete convergence, can quickly search the best center of classification clustering at high accuracy. Therefore, our method M-CLONALG is superior to other three algorithms in remote sensing image information extracting, and its overall accuracy and Kappa statistic reach 91.7% and 0.89 respectively.
Starting Page	94
Ending Page	102
File Size	2328272
Page Count	9
File Format	PDF
ISBN	9780769535715
DOI	10.1109/GCIS.2009.155
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability Information extracting Artificial intelligence algorithm Optimization methods Data mining M-CLONALG Remote sensing Satellites Information processing Remote sensing (RS) Classification CLONALG Problem-solving Artificial intelligence Power engineering and energy Testing
Content Type	Text
Resource Type	Article

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