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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 114-120     DOI: 10.6046/zrzyyg.2020301
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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
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Abstract  

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.
Keywords K-means clustering      UAV remote sensing      threshold-based segmentation      image classification      MATLAB/GUI     
ZTFLH:  TP751  
Corresponding Authors: WANG Zhangqiong     E-mail: 2502567737@qq.com;wzqcug@163.com
Issue Date: 24 September 2021
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Junlong BAI
Zhangqiong WANG
Haitao YAN
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Junlong BAI,Zhangqiong WANG,Haitao YAN. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020301     OR     https://www.gtzyyg.com/EN/Y2021/V33/I3/114
Fig.1  Schematic diagram of the relationship between the number of clusters and the Average Silhouette value
Fig.2  The process of threshold classification under K-means clustering guidance
Fig.3  UAV remote sensing image processing system interface
Fig.4  UAV remote sensing image after stitching
Fig.5  Relationship between the number of clusters and the Average Silhouette value
Fig.6  K-means clustering initial segmentation results
Fig.7  Image combination and cutout
Fig.8  GUI tool for image threshold selection
Fig.9  Optimized binary image
Fig.10  Sub interface of image processing system
分类后土地利用类型 真实土地利用类型
林地、草地 水域 交通运输用地 其他土地 行总计 UA/%
林地、草地 148 2 0 14 164 90.24
水域 0 91 0 1 92 98.91
交通运输用地 0 0 68 1 69 98.55
其他土地 13 5 4 102 124 82.26
列总计 161 98 72 118 449 —
PA/% 91.93 92.86 94.44 86.44 — —
OA=91.09%; Kappa系数=0.88
Tab.1  Confusion matrix
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