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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Comparison of four Machine Learning methods for Object-Oriented Change Detection in High-Resolution Satellite Imagery
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Comparison of four Machine Learning methods for Object-Oriented Change Detection in High-Resolution Satellite Imagery

机译：高分辨率卫星影像中面向对象变化检测的四种机器学习方法的比较

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摘要

High resolution image change detection is one of the key technologies of remote sensing application, which is of great significance for resource survey, environmental monitoring, fine agriculture, military mapping and battlefield environment detection. In this paper, for high-resolution satellite imagery, Random Forest (RF), Support Vector Machine (SVM), Deep belief network (DBN), and Adaboost models were established to verify the possibility of different machine learning applications in change detection. In order to compare detection accuracy of four machine learning Method, we applied these four machine learning methods for two high-resolution images. The results shows that SVM has higher overall accuracy at small samples compared to RF, Adaboost, and DBN for binary and from-to change detection. With the increase in the number of samples, RF has higher overall accuracy compared to Adaboost, SVM and DBN.
机译：高分辨率图像变化检测是遥感应用的关键技术之一，对资源调查，环境监测，精细农业，军事制图和战场环境检测具有重要意义。在本文中，对于高分辨率卫星图像，建立了随机森林（RF），支持向量机（SVM），深度信念网络（DBN）和Adaboost模型，以验证在变化检测中使用不同机器学习应用程序的可能性。为了比较四种机器学习方法的检测精度，我们将这四种机器学习方法应用于两个高分辨率图像。结果表明，与RF，Adaboost和DBN相比，SVM在小样本上具有更高的整体精度，可用于二进制和从-到-变化检测。随着样本数量的增加，与Adaboost，SVM和DBN相比，RF具有更高的整体精度。

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