Mr. Liming Pan Profile

Liming Pan

at Jilin Univ

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Proceedings Article | 20 August 2020 Poster + Paper

Land cover classification based on machine learning using UAV multi-spectral images

Liming Pan, Lingjia Gu, Ruizhi Ren, Shuting Yang

Proceedings Volume 11501, 115011F (2020) https://doi.org/10.1117/12.2566128

KEYWORDS: Image classification, Unmanned aerial vehicles, Machine learning, Image segmentation, Remote sensing, Spatial resolution, Analytical research, Near infrared, Image resolution

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Land cover classification using UAV multi-spectral images is of great significance in precision agriculture, urban planning, land use and other fields. However, traditional remote sensing image classification methods cannot meet the classification accuracy requirements of UAV multi-spectral images. This paper aims to propose an object-based machine learning classification method to improve the land over classification accuracy of UAV multi-spectral images. The experimental area is a standard test field located in the Jilin Province of China. The experimental data was captured by a UAV equipped with a multi-spectral camera which includes four bands from 550 nm to 790 nm. First, the original images were preprocessed and the spectral curves of land cover were analyzed, thus four kinds of land cover with large differences were selected as categories. Then pixel-based, boosting-based and object-based machine learning methods were used for classification. The object-based classification method could make full use of the spatial and spectral information, and eliminate the noise problem caused by the high resolution of the UAV image to a certain extent. Finally, accuracy analysis using the verification image showed that the RF-O method achieved the highest classification accuracy of 92.2419%, and the kappa coefficient was 0.8904. All results indicate that the object-based machine learning classification method proposed in this paper is more suitable for the research of land cover classification, comparing with the traditional remote sensing image classification methods, and performs well on the land cover classification of UAV multi-spectral images.

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