| 顾及多尺度分割参数的FNEA面向对象分类 |
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| 引用本文： | 孙坤,鲁铁定.顾及多尺度分割参数的FNEA面向对象分类[J].测绘通报,2018,0(3):43-48. |
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| 作者姓名： | 孙坤 鲁铁定 |
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| 作者单位： | 1. 东华理工大学测绘工程学院, 江西 南昌 330013;
2. 流域生态与地理环境监测国家测绘地理信息局重点实验室, 江西 南昌 330013 |
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| 基金项目： | 国家自然科学基金，测绘地理信息公益性行业科技专项，国家重点研发计划，国家重大科学研究计划，江西省教育厅科技项目 |
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| 摘 要： | 首先对分形网络演化（FNEA）算法做了介绍，通过试验对比4种分割方法棋盘分割、四叉树分割、多尺度分割、光谱差异分割的效果。此外，通过eCognition Developer 8.7分析软件对影像进行多尺度分割预处理，从5开始，以5为单位向上递增，共选取12组参数进行分割试验，采用面向对象CART分类器对分割后影像分类。对比分类效果图可知，小尺度分割参数对分类效果能有较好的提升；对比总体精度及Kappa系数可知，小尺度分割参数分类精度优于大尺度分割参数，且当分割参数Scale为10时，分类精度达到最好的级别。
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| 关 键 词： | FNEA 分割方法 多尺度分割 参数选取 CART分类器 |
| 收稿时间： | 2017-06-19 |
Research on FNEA Object-oriented Classification Based on Multi-scale Partition Parameters |
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| SUN Kun,LU Tieding.Research on FNEA Object-oriented Classification Based on Multi-scale Partition Parameters[J].Bulletin of Surveying and Mapping,2018,0(3):43-48. |
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| Authors: | SUN Kun LU Tieding |
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| Institution: | 1. Faculty of Geomatics, East China University of Technology, Nanchang 330013, China;
2. Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang 330013, China |
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| Abstract: | In this paper,the fractal net evolution approach ( FNEA) is introduced,and then the effects of four segmentation methods, chessboard segmentation,quadtree-based segmentation,multiresolution segmentation and spectral difference segmentation are compared. In addition,the eCognition Developer 8. 7 analysis software is used to perform multi-scale segmentation preprocessing of the image, starting from 5 and increasing in 5 increments. A total of 12 sets of parameters are selected for segmentation experiments. The object-oriented CART classifier is used to classify the segmented image.Compared with the overall accuracy and Kappa coefficient,it can be seen that the classification accuracy of small-scale segmentation parameters is better than that of large-scale segmentation parameters. When the segmentation parameter scale is 10, the small-scale segmentation parameters can be better than the large-classification accuracy to achieve the best level. |
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| Keywords: | fractal net evolution approach segmentation method multi-scale segmentation parameter selection CART classifier |
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