A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images
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摘要: 针对无人机获取的高分辨率遥感图像分类需求,提出一种K-means聚类引导的阈值分类方法。首先计算出无人机遥感图像数据集的Average Silhouette值,作为K-means的最优聚类数目; 然后对原始图像进行K-means聚类初分割,对初分割结果中的非目标区域进行手工剔除; 再对处理之后的新对象进行阈值分割和图像优化,完成对象的提取; 最后对所有处理得到的地物标签进行合并,实现遥感图像的识别与分类。基于MATLAB/GUI平台,对提出的分类方法处理步骤进行集成,开发了无人机遥感图像分类处理系统,可对无人机遥感图像进行快速处理,实现半自动解译。对分类结果进行精度验证,其总体精度为91.09%,Kappa系数为0.88,表明该方法用于无人机遥感图像分类处理,能够实现地物的精确分类与信息提取。
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关键词:
- K-means聚类 /
- 无人机遥感 /
- 阈值分割 /
- 图像分类 /
- MATLAB/GUI
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.-
Key words:
- K-means clustering /
- UAV remote sensing /
- threshold-based segmentation /
- image classification /
- MATLAB/GUI /
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