8 March 2018 Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery
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Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106110G (2018) https://doi.org/10.1117/12.2285493
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
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.
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Ting Bai, Kaimin Sun, Shiquan Deng, Yan Chen, "Comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106110G (8 March 2018); doi: 10.1117/12.2285493; https://doi.org/10.1117/12.2285493
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