A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images
BAI Junlong1(), WANG Zhangqiong1(), YAN Haitao2
1. School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430073, China 2. CCCC Second Highway Consultants, Co., Ltd., Wuhan 430052, China
This study proposed a K-means clustering-guided threshold-based approach to classifying the high-resolution remote sensing images obtained using unmanned aerial vehicles (UAVs). The steps of the approach are as follows. First, calculate the average silhouette of the UAV remote sensing image dataset as the optimal number of clusters in the K-means clustering. Then perform K-means clustering on the original images, and manually remove non-target areas in the initial segmentation results. Afterward, perform threshold-based segmentation and image optimization on the new objects obtained to extract objects. Finally, combine all the feature tags obtained to realize the recognition and classification of remote sensing images. The abovementioned processing steps were integrated using the MATLAB/GUI platform. Based on this, a classification processing system of UAV remote sensing images was developed. It can quickly process UAV remote sensing images and achieve semi-automatic interpretation. The accuracy of the classification results was verified, obtaining an overall accuracy of 91.09% and a Kappa coefficient of 0.88. This indicates that the approach proposed in this paper can obtain high-quality segmentation results of UAV remote sensing images.
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2021, Vol. 33 
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