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Study on a New Remote Sensing Image Classification Method and Its Application

By Wu Deng, Danqin Wang, and Huimin Zhao
Progress In Electromagnetics Research C, Vol. 84, 215-226, 2018


For slower computation speed and lower classification accuracy of the traditional image classification methods, wavelet transform, multi-strategy, particle swarm optimization (PSO) algorithm and support vector machine (SVM) are introduced into image classification in order to propose a new remote sensing image classification (RIWMPS) method. First of all, wavelet transform method with multi-resolution characteristics is used to extract the features of remote sensing image. Then the steepest descent strategy, corrective decline strategy, random movement, aggregation strategy and diffusion strategy are used to improve the PSO algorithm to obtain an improved PSO (MSPSO) algorithm, which is used to optimize the parameters of the SVM model in order to construct an optimized SVM classifier for realizing remote sensing classification. Finally, the remote sensing image of Chongming Island is select to test the effectiveness of the RIWMPS method. The experiment results show that the RIWMPS method has higher classification efficiency and accuracy, and takes on better superiority and effectiveness. This study provides a new classification method for processing the remote sensing image.


Wu Deng, Danqin Wang, and Huimin Zhao, "Study on a New Remote Sensing Image Classification Method and Its Application," Progress In Electromagnetics Research C, Vol. 84, 215-226, 2018.


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