Abstract
In order to improve the classification accuracy of high resolution change detection, the key technologies such as segmentation scale determination, features selection and classifier use are studied, and a change detection method using hierarchical decision tree is proposed. Firstly, fractal net evolution approach was used to obtain image objects, and the optimal scales of vegetation, water, and man-made objects were determined by evaluation index based on classification accuracy. Second, the feature spaces of man-made, water and vegetation objects are constructed. Then, the hierarchical decision tree classification method with the optimal segmentation scales is applied to multi-temporal high resolution remote sensing images. Finally, change detection was implemented by comparing the multi-temporal classification results. The multi-temporal high resolution remote sensing images in Wuhan Lujiazhuang were chosen as the experimental data. The experiments show that the method is effective.
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Xie, Z., Wang, M., Han, Y., Yang, D. (2019). Hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images. In: Xie, Y., Zhang, A., Liu, H., Feng, L. (eds) Geo-informatics in Sustainable Ecosystem and Society. GSES 2018. Communications in Computer and Information Science, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-13-7025-0_18
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DOI: https://doi.org/10.1007/978-981-13-7025-0_18
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