| Technology and Methodology |
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Supervised evaluation of optimal segmentation scale with object-oriented method in remote sensing image |
| ZHUANG Xiyang1,2, ZHAO Shuhe1,2,3, CHEN Cheng1,4, CONG Dianmin1,2, QU Yongchao1,2 |
1. Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China;
2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China;
4. Nanjing Hydraulic Research Institute, Nanjing 210029, China |
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Abstract The object-oriented classification quality of the remote sensing images depends not only on the classification algorithm but also on the goodness of the segmentation results. The quality of image segmentation determines the accuracy of subsequent classification of the remote sensing images. The quantitative method for determining the optimal segmentation scale and eliminating the interference of subjective factors becomes the focus of the image segmentation quality assessment. However, the importance of object recognition in image segmentation quality evaluation is often ignored in the previous segmentation quality evaluation method. After analyzing the complex spatial relations between the image objects and the actual image region, a new optimal segmentation scale evaluation index based on the area and position of the image object was proposed to evaluate the optimal segmentation scale. Based on the evaluation index, a WorldView2 multispectral image was used to be researched and the optimal segmentation parameters were determined. The results show that the segmentation scale evaluation index is effective in image segmentation quality assessment and parameter optimization. The experimental results have also shown the effectiveness of the method proposed in this paper for both segmentation quality assessment and optimal parameter selection. Also, the procedure of segmentation quality assessment can be conducted with less human intervention, making the result more objective.
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| Keywords
soil moisture retrieval
active microwave remote sensing
bare soil
vegetation cover
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Issue Date: 20 October 2016
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2016,
Vol. 28
