基于Google Earth图像的Naïve贝叶斯分类器对绿色开放空间目标的分类优化

I. Santoso, Supriyono, Cahyo Crysdian, Khadijah Fahmi Hayati Holle
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引用次数: 1

摘要

洪水、空气污染和气温上升对城市社会构成了明显的威胁。这些问题是由不受控制的人类活动引起的,并导致环境破坏。解决这一问题的方法是使用计算机化的设施,以绿色开放空间的形式监测真实的环境状况。免费和可获得的谷歌地球图像使提供这些设施成为可能。然而,谷歌earth提供的信息仅显示卫星照片,无法用于地球表面绿色开放空间的目标分类。因此,有必要开发利用谷歌地球免费和可获得的数据对地球表面,特别是城市地区的物体进行分类的方法。本研究的案例研究包括印度尼西亚的几个城市,采用基于图像的绿色开放空间分类方法为naïve贝叶斯分类器(NBC)。本文采用训练和测试两个阶段对绿色开放空间进行分类。训练过程是构建一个由多个NBC模型组成的新的NBC模型结构。而测试过程则是根据结构对绿色开放空间进行分类。利用测试样本进行的实验结果表明,新结构的NBC模型在绿色开放空间分类中的准确率优于NBC单一模型
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of Naïve Bayes Classifier To Classify Green Open Space Object Based on Google Earth Image
Floods, air pollution, and increasing air temperatures become a visible danger for society in urban areas. These problems are caused by uncontrolled human activities and lead to environmental damage. To resolve this problem is by using computerized facilities that can monitor real environmental condition in the form of green open space. The provision of such facilities is enabled by free and accessible Google earth image. However, the information provided by Google earth only shows satellite photos that cannot be used for objects classification of green open space on the surface of the earth. Therefore, it is necessary to develop methods to classify objects of earth's surface, particularly in urban area, using free and available data from Google earth. The case study of this research includes several cities in Indonesia, while the method employed to classify image-based green open space was naïve bayes classifier (NBC). In this paper, training and testing are among two stages used in classifying green open space. Training process is to construct a new structure of NBC model which involves several NBC models. While testing process is to classify green open space based on the structure. The experiment results using the test sample show that the accuracy of the new structure of NBC model in the green open space classification is better than the NBC single model
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