Self-adapting differential evolution and its application on remote sensing image supervised classification

Jia Wu, Zhihua Cai, Xiaowen Jin

Research output: Contribution to journalArticlepeer-review

Abstract

A new classification algorithm is proposed for remote sensing imagery based on the self-adapting differential evolution to be waged against the three main disadvantages of traditional classification algorithm for remote sensing image:multiple constraints,easy to fall into local optimal solution,lower classification accuracy.In the new method for supervised classification of multi/hyper-spectral remote sensing image,the globally optimal cluster centers are firstly learned by using the self-adapting differential evolution algorithm,and then the whole remote sensing image can be classified by the cluster centers.The proposed algorithm for classification of remote sensing image is based on the standard differential evolution.The experimental results show that the self-adapting differential evolution clustering algorithm has higher classification accuracy than the traditional classification algorithm of remote sensing image.The classification accuracy and the kappa coefficient can reach 92.66% and 0.901 7,which has some practical application value.
Original languageEnglish
Pages (from-to)23-26
Number of pages4
JournalGeomatics and Information Science of Wuhan University
Volume38
Issue number1
Publication statusPublished - 2013
Externally publishedYes

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