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- W4378976278 abstract "Accurate classification of wetland vegetation types is the basis for monitoring and management of wetland ecosystems. At present, the recognition accuracy of wetland vegetation classification based on UAV remote sensing images is not high. In this paper, to address this problem, 17 feature variables contained in spectrum, spatial geometry and texture were firstly selected; then the improved JM-Relief F algorithm was used to rank, judge and select the weights of the constructed feature variables, and remove the redundant variables; finally, the preferred 8 feature variables were classified by random forest, support vector machine and K-nearest neighbor models, respectively. The results showed that the R, G, and B band averages, asymmetry, GLCM homogeneity, GLCM contrast, GLCM correlation, and GLCM standard deviation obtained by feature optimization were the best features for classifying wetland vegetation, which could adequately represent the image features while improving the classification accuracy. The highest classification accuracy was achieved using the random forest model, with an overall classification accuracy of 88.80% and a Kappa coefficient of 84.3%, followed by the support vector machine and K-nearest neighbor models. The improved JM-Relief F feature selection algorithm combined with random forest classification model method can be effectively applied to the application of wetland vegetation classification and identification in the intertidal zone." @default.
- W4378976278 created "2023-06-02" @default.
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- W4378976278 date "2023-06-01" @default.
- W4378976278 modified "2023-09-25" @default.
- W4378976278 title "Wetland vegetation identification based on improved JM-Relief F feature optimization algorithm" @default.
- W4378976278 doi "https://doi.org/10.1117/12.2682653" @default.
- W4378976278 hasPublicationYear "2023" @default.
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