| 基于Landsat-8与ZY3多源遥感影像的城镇居民地识别与再分类研究 |
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| 引用本文： | 李振敏,王晓青,窦爱霞,杨海霞,黄树松,崔丽萍.基于Landsat-8与ZY3多源遥感影像的城镇居民地识别与再分类研究[J].地震,2015,35(3):123-135. |
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| 作者姓名： | 李振敏 王晓青 窦爱霞 杨海霞 黄树松 崔丽萍 |
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| 作者单位： | 中国地震局地震预测研究所, 北京 100036 |
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| 基金项目： | “十二五”科技支撑课题 |
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| 摘 要： | 精细的居民地数据对地震灾害风险分析具有重要意义。 为得到具有较高时效性与精细度的居民地数据, 充分发挥其对人口、 建筑物空间展布的指示作用, 本文综合利用多源遥感影像的优势, 基于分层分类思想开展城镇居民地识别与再分类研究。 以甘肃天水秦州区的主城区为例, 采用具有较高时效性的Landsat-8 OLI影像, 建立决策树分类模型识别出居民地轮廓; 在居民地轮廓内部, 进一步采用资源三号卫星(ZY3)高分影像, 利用面向对象方法进行居民地内部的建筑群再分类, 最后得到了具有不同精细程度的居民地数据。 实验结果中Landsat-8土地覆盖分类总体精度为92%(其中居民地识别率达86%), 城镇居民地再分类的总体精度为81%, 说明了本文研究方案的可行性。
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| 关 键 词： | 居民地遥感识别 分层分类 Landsat-8 地震风险分析 |
| 收稿时间： | 2015-01-27 |
Extraction and Re-classification of Urban Residential Area Based on Landsat-8 and ZY3 Multi-source Remote Sensing Images |
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| LI Zhen-min
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WANG Xiao-qing
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DOU Ai-xia
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YANG Hai-xia
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HUANG Shu-song
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CUI Li-ping.Extraction and Re-classification of Urban Residential Area Based on Landsat-8 and ZY3 Multi-source Remote Sensing Images[J].Earthquake,2015,35(3):123-135. |
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| Authors: | LI Zhen-min WANG Xiao-qing DOU Ai-xia YANG Hai-xia HUANG Shu-song CUI Li-ping |
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| Institution: | Institute of Earthquake Science, CEA, Beijing 100036, China |
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| Abstract: | Detailed residential area (ResA) distribution data have significances to seismic risk analysis. In order to obtain more detailed ResA data with higher timeliness, and give their full play in indicating the spatial distribution of population and buildings, we integrated the advantages of multi-source RS images, and carried out the extraction and re-classification research of urban ResA based on hierarchical classification method. With Qinzhou District, Tianshui, Gansu Province of China as study area, we used new Landsat-8 OLI image to classify urban land-use and recognize the holistic ResA through building a decision tree classification model. Inside the urban ResA, we further used domestic ZY3 satellite image to re-classify the land-use and buildings based on object-oriented method. Finally the ResA data with different levels of detail were obtained. The overall land-use classification precision of Landsat-8 image was about 92% (the recognition rate of ResA was 86%), and the re-classification precision of buildings inside urban ResA is 81% (only consumed half of the time). It indicated that based on multi-source RS images, the hierarchical classification method is available to extract and re-classify urban ResA. |
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| Keywords: | Residential area extraction Earthquake risk analysis Hierarchical classification Landsat-8 |
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