A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images

doi:10.6046/zrzyyg.2020301

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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

Cite this article:

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.

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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

Tab.1 Confusion matrix

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