Abstract
In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB), random trees (RT), and support vector machine (SVM). For pixel-based classification, we utilized the maximum likelihood classifier (MLC). Specifically, we explored the influence of repeated sampling on classification results with different training sample sizes. We found that (1) except the overall accuracy of NB, those of the other four classifiers increased as the training sample size increased; (2) repeated sampling had a significant effect on classification accuracy, especially for the DT and NB classifiers; and (3) SVM achieved the best classification accuracy. In addition, the performance of the object-based classifiers was superior to that of the pixel-based classifier. The results of this study can provide guidance on the training sample size and classifier selection.
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Acknowledgements
The authors wish to thank the editor. The authors also thank Qian Yuguo for his help in operation of eCognition. This work was supported by National Key R&D Program of China [No. 2017YFB0504000, No. 2017YFB0503805]; Special Project on High Resolution of Earth Observation System for Major Function Oriented Zones Planning [No. 00-Y30B14-9001-14/16].
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Shang, M., Wang, SX., Zhou, Y. et al. Effects of Training Samples and Classifiers on Classification of Landsat-8 Imagery. J Indian Soc Remote Sens 46, 1333–1340 (2018). https://doi.org/10.1007/s12524-018-0777-z
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DOI: https://doi.org/10.1007/s12524-018-0777-z