| 高分辨率遥感影像分割的城市绿地提取研究 |
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| 引用本文： | 陈周,费鲜芸,高祥伟,王筱雪,赵慧敏.高分辨率遥感影像分割的城市绿地提取研究[J].测绘通报,2020,0(12):17-20. |
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| 作者姓名： | 陈周 费鲜芸 高祥伟 王筱雪 赵慧敏 |
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| 作者单位： | 江苏海洋大学测绘与海洋信息学院, 江苏 连云港 222005 |
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| 基金项目： | 国家自然科学基金（31270745）；江苏省研究生科研创新计划（SJCX19_0969） |
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| 摘 要： | 城市绿地是生态文明建设的重要组成部分，绿地信息提取是城市绿地规划和建设的基础和前提。遥感影像分割是绿地信息分类提取的关键步骤，选择合适的影像分割方法能有效提高城市绿地提取精度。传统的遥感分割方法分割结果中边缘锯齿现象严重，与绿地实地边界相差较大，不符合绿地信息提取的要求。本文以高分辨率的WorldView影像为数据源，使用深度学习网络DeepLab-v3+对城市绿地进行分割研究，在分割基础上进行城市绿地信息提取。同时，本研究将该网络模型的分割和分类结果与基于Ostu、MeanShift、FNEA分割算法的分类精度进行比较。研究表明：DeepLab-v3+的分割性能最好，其分割边缘光滑，与绿地实地边界吻合度高，有效解决了传统分割算法的边缘锯齿问题；在各种分割分类算法中，DeepLab-v3+的分类精度最高，达到98.01%。
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| 关 键 词： | 城市绿地 Ostu算法 MeanShit算法 FNEA算法 DeepLab-v3+ |
| 收稿时间： | 2020-06-30 |
| 修稿时间： | 2020-07-31 |
Extraction of urban green space with high resolution remote sensing image segmentation |
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| CHEN Zhou,FEI Xianyun,GAO Xiangwei,WANG Xiaoxue,ZHAO Huimin.Extraction of urban green space with high resolution remote sensing image segmentation[J].Bulletin of Surveying and Mapping,2020,0(12):17-20. |
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| Authors: | CHEN Zhou FEI Xianyun GAO Xiangwei WANG Xiaoxue ZHAO Huimin |
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| Institution: | School of Geomatics and Marine Information, Jiangsu Ocean University, Lianyungang 222005, China |
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| Abstract: | Urban green space is an important part of ecological civilization construction, and the extraction of green space information is the basis and premise of urban green space planning and construction. Remote sensing image segmentation is the key step of green space information classification. Selecting appropriate image segmentation method can effectively improve the accuracy of urban green space extraction. The traditional remote sensing segmentation method results in a serious edge jagged phenomenon, which is quite different from the green field boundary, and does not meet the requirements of green space information extraction. In this paper, the high-resolution Worldview image is used as the data source, and the urban green space information is extracted based on the deep learning network DeepLab-v3+. At the same time, the classification accuracy of the proposed algorithm is compared with that of Ostu, MeanShift and FNEA. The results show that: DeepLab-v3 + has the best segmentation performance, its segmentation edge is smooth, and it has a high degree of coincidence with the green field boundary, which can effectively solve the edge jagged problem of traditional segmentation algorithm. Because of the good segmentation effect, it also effectively improves the classification accuracy. Among various segmentation and classification algorithms, the accuracy of DeepLab-v3 + is the highest, reaching 98.01%. |
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| Keywords: | urban green space Ostu algorithm MeanShift algorithm FNEA algorithm DeepLab-v3+ |
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