A method of building extraction combing RF feature selection with the SVM model

Yuqi Zhang; Xianglei Liu

doi:10.1117/12.3019823

23 January 2024 A method of building extraction combing RF feature selection with the SVM model

Yuqi Zhang, Xianglei Liu

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 1297824 (2024) https://doi.org/10.1117/12.3019823
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

The extraction of building information is of great significance for environmental change detection and urban development, and is also conducive to the country's macro-control and scientific management. Based on the four Landsat8 OLI image data of Huangdao District, Qingdao City in 2015, 2017, 2019 and 2021, this study proposes a building extraction method combing random forest feature selection with SVM algorithm. In this method, first, 22 feature variables are extracted from the images. Second, the random forest is used to screen out the important features. Finally, the selected feature variables are combined with the SVM classifier. In this study, ten features out of 22 features are screened out by RF, and their feature importance reach 94.37%. Moreover, the building extraction effect after feature selection is significantly better than that before selection. The results have shown that the overall accuracy and Kappa of the image classification results based on SVM in each time phase are higher than those based on other machine learning algorithms, and the overall classification accuracy and Kappa are both 96% and 0.95 or more.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuqi Zhang and Xianglei Liu "A method of building extraction combing RF feature selection with the SVM model", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 1297824 (23 January 2024); https://doi.org/10.1117/12.3019823

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KEYWORDS

Feature extraction

Feature selection

Machine learning

Random forests

Landsat

Vegetation

Image classification

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