基于k均值聚类和区域生长的彩色图像分割计算红树林面积

R. Rizal Isnanto, Oky Dwi Nurhayati, Tyas Panorama Nan Cerah
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引用次数: 1

摘要

用传统方法计算红树林面积需要耗费大量的时间和精力。本研究基于k-means聚类和区域生长两种分割方法,开发了一种基于卫星影像计算印尼苏拉威西省东南部红树林面积的工具。然后对这两种方法进行比较,得出计算红树林面积的最优方法。在此研究之前,没有研究人员使用这两种方法计算苏拉威西东南部红树林的面积。我们用Matlab构建了一个计算算法,其中包含了数字图像处理的不同阶段。红树林面积按像元数计算,面积密度为900 m2/像元。比较了两种分割方法在印度尼西亚国家航空航天研究所(LAPAN)获得的相同区域的分割精度,即以LAPAN获得的区域作为计算精度的参考。在12个聚类的应用中,区域增长分割方法的分割准确率为33.33%,而k均值聚类分割方法在最优条件下的分割准确率为59.26%。
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Area of Mangrove Forests Calculated by Color Image Segmentation using K-Means Clustering and Region Growing)
The calculation of the area of mangrove forests by conventional methods requires much time and energy. In this study, a tool for calculating the area of mangrove forests in Southeast Sulawesi Province, Indonesia, using satellite imagery is developed on the basis of two segmentation methods, k-means clustering and region growing. We then compare those two methods to obtain the optimal method to calculate the area of mangrove forests. Before this research, there were no researchers who calculated the area of mangrove forests in Southeast Sulawesi using both methods. We constructed a calculation algorithm using Matlab, which includes different stages of digital image processing. The area of mangrove forests is calculated on the basis of the number of pixels with an area density of 900 m2/pixel. The accuracy of the two segmentation methods is compared for identical areas obtained by the National Institute of Aviation and Space in Indonesia (LAPAN), i.e., the area obtained by LAPAN is used as a reference in calculating the accuracy. The accuracy of the region growing segmentation method is 33.33%, whereas that by the k-means clustering segmentation method under optimum conditions is 59.26% in the application of 12 clusters.
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