Wetland vegetation identification based on improved JM-Relief F feature optimization algorithm

Xiang Chen; Shoujun Li; Ziwei Zhang; Deyu Miao

doi:10.1117/12.2682653

1 June 2023 Wetland vegetation identification based on improved JM-Relief F feature optimization algorithm

Xiang Chen, Shoujun Li, Ziwei Zhang, Deyu Miao

Proceedings Volume 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023); 127100T (2023) https://doi.org/10.1117/12.2682653
Event: International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 2023, Changsha, China

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.

Citation Download Citation

Xiang Chen, Shoujun Li, Ziwei Zhang, and Deyu Miao "Wetland vegetation identification based on improved JM-Relief F feature optimization algorithm", Proc. SPIE 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 127100T (1 June 2023); https://doi.org/10.1117/12.2682653

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KEYWORDS

Vegetation

Image segmentation

Image classification

Mathematical optimization

Remote sensing

Unmanned aerial vehicles

Education and training

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