| COSMO-SkyMed颜色变换强度RC法南京建设用地变化检测 |
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| 引用本文： | 张涛,王源,陈富龙,周伟,胡祺.COSMO-SkyMed颜色变换强度RC法南京建设用地变化检测[J].测绘通报,2019,0(11):74-78,84. |
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| 作者姓名： | 张涛 王源 陈富龙 周伟 胡祺 |
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| 作者单位： | 南京市规划和自然资源局,江苏 南京,210029;中国科学院遥感与数字地球研究所数字地球重点实验室,北京100094;中国科学院大学,北京100049;中国科学院遥感与数字地球研究所数字地球重点实验室,北京,100094 |
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| 基金项目： | 国家自然科学基金（41771489）；国家重点研发计划（2016YFB0501502） |
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| 摘 要： | 基于非局部滤波的SAR强度RC合成变化检测法对小图斑、线型地物等动态监测灵敏，且对数据获取无时空基线要求，在多云多雨城市地表要素变化检测中具备潜力。本文研究以多时相SAR强度RC合成图为数据源，提出一种基于色彩空间变换的变化图斑半自动提取方法，即通过色彩空间转换、训练样本选取、监督分类影像分割、变化区域提取4步骤，可实现基于SAR强度图的城市建设用地动态监测与图斑高效更新。选取南京河西新城与江北新区为示范，以最优参数配置（3特征向量与10样本类别）进行试验，实现了优于88%的建设用地查准率指标。
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| 关 键 词： | 城市变化检测 SAR强度RC合成 色彩空间变换 COSMO-SkyMed 建设用地 |
| 收稿时间： | 2019-04-04 |
Nanjing City urban change detection using the color-space transformation of COSMO-SkyMed intensity RC composition imagery |
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| ZHANG Tao,WANG Yuan,CHEN Fulong,ZHOU Wei,HU Qi.Nanjing City urban change detection using the color-space transformation of COSMO-SkyMed intensity RC composition imagery[J].Bulletin of Surveying and Mapping,2019,0(11):74-78,84. |
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| Authors: | ZHANG Tao WANG Yuan CHEN Fulong ZHOU Wei HU Qi |
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| Institution: | 1. Nanjing Bureau of Planning and Natural Resources, Nanjing 210029, China;2. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;3. University of Chinese Academy of Sciences, Beijing 100049, China |
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| Abstract: | The developed change detection method, by utilizing the non-local filtered SAR intensity RC composition, is sensitive in the extraction of small patches and linear features. Consequently, it will indicate a better performance in practical applications, in particular this method is not constrained by additional requirements, e.g. the spatiotemporal baseline. In this study, taking the intensity RC composite imagery as the data-source, a semi-automatic change detection method is proposed by utilizing color-transformed features. In order to realize the thematic updating of urban-area land, the corresponding data procedures include four primary steps, they are color space transformation, training-sample selection, supervised classification based image segmentation, and change region extraction. Taking Hexi New Town and Jiangbei Developing District (Nanjing) as example, the checking probability is better than 88% with the optimum parameter setting (3 features along with 10 training-sample categories). |
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| Keywords: | urban change detection SAR intensity RC composition color space transformation COSMO-SkyMed construction land-use |
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