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On the application of remote sensing towards the estimation of cultivated land lost to urbanization

机译:关于遥感对城市化造成耕地估算的应用

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In this research work, a 40-km(2) SPOT-5 High-Resolution Imagery (HRI) of the Warsak locality in district Peshawar, Pakistan, was utilized to approximate the quantity of cultivated land lost to urbanization, due to the construction of new homes and buildings. The imagery from a period of 2005 to 2015 for wheat crop was taken, specifically during the months of March and June when the crop is rich green and golden ripe respectively. eCognition (R) program's Object-Oriented Classification Method (OOCM) was employed for recognition of land versus buildings. Nearest Neighbour (NN), Support Vector Machine (SVM), Decision Trees (DT) and Random Forests (RF) were utilized for the classification process. The results demonstrated that the urbanized area had increased by approximately 28 per cent in the area considered. Moreover, the efficacy of the proposed method is depicted by an accuracy of 97.9 per cent and a Kappa Statistics of 0.975 for the SVM classifier.
机译:在这项研究工作中,巴基斯坦区彭川区的华沙地区的40公里(2)个现货 - 5个高分辨率图像(HRI)被利用,由于建设,将近似于城市化造成的耕地量。新房和建筑物。从2005年到2015年的小麦作物的图像采取,特别是在3月和6月份,当农作物分别是丰富的绿色和金色成熟的。认知(R)计划的面向对象的分类方法(OOCM)用于识别土地与建筑物。用于分类过程,利用最近的邻居(NN),支持向量机(SVM),决策树(DT)和随机森林(RF)。结果表明,城市化面积在考虑的地区增加了约28%。此外,所提出的方法的功效被SVM分类器的97.9%的精度和0.975的κ统计所示。

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