结合像元级和目标级的高分辨率遥感影像建筑物变化检测

doi:10.11947/j.AGCS.2018.20170483

测绘学报 ›› 2018, Vol. 47 ›› Issue (1): 102-112.doi: 10.11947/j.AGCS.2018.20170483

结合像元级和目标级的高分辨率遥感影像建筑物变化检测

张志强^1,3, 张新长^2,1, 辛秦川^1,3, 杨晓羚⁴

1. 中山大学地理科学与规划学院, 广东广州 510275;
2. 广州大学, 广东广州 510006;
3. 广东省城市化与地理环境空间模拟重点实验室, 广东广州 510275;
4. 广东省城乡规划设计研究院, 广东广州 510290

收稿日期:2017-08-30 修回日期:2017-11-17 出版日期:2018-01-20 发布日期:2018-02-05
通讯作者: 张新长 E-mail:eeszxc@mail.sysu.edu.cn
作者简介:张志强(1987-),男,博士生,研究方向为遥感信息提取、基础地理空间数据更新。E-mail:zhangzhiqiang.8866@163.com
基金资助:
国家自然科学基金重点项目（41431178）；国家自然科学基金面上项目（41671453）；广东省自然科学基金重点项目（2016A030311016）；中山大学高校基本科研业务费青年教师重点培育项目（17lgzd02）；智慧广州时空信息云平台建设项目（GZIT2016-A5-147）

Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

ZHANG Zhiqiang^1,3, ZHANG Xinchang^2,1, XIN Qinchuan^1,3, YANG Xiaoling⁴

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
2. Guangzhou University, Guangzhou 510006, China;
3. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, Guangzhou 510275, China;
4. Guangdong Urban & Rural Planning and Design Institute, Guangzhou 510290, China

Received:2017-08-30 Revised:2017-11-17 Online:2018-01-20 Published:2018-02-05
Supported by:
The National Natural Science Foundation of China (Nos. 41431178;41671453);The National Natural Science Foundation of Guangdong Province of China (No. 2016A030311016);Key Projects for Young Teachers at Sun Yat-sen University (No. 17lgzd02);The National Administration of Surveying,Mapping and Geoinformation of China (No. GZIT2016-A5-147)

摘要/Abstract

摘要： 快速、精准的建筑物变化检测对城市规划建设等业务管理具有重要意义。随着卫星遥感技术的快速发展，基于高分辨率遥感影像的建筑物变化检测得到了广泛关注。针对像元级建筑物变化检测方法往往精度不足而目标级建筑物变化检测方法过程烦琐等问题，本文提出结合像元级和目标级的高分辨率遥感影像建筑物变化检测方法。首先综合高分辨率遥感影像的多维特征，利用随机森林分类器进行影像集分类，以获取像元级建筑物变化检测结果；然后对后时相遥感影像进行图像分割，获得影像对象；最后融合像元级建筑物变化检测结果和影像对象，识别变化的建筑物目标。利用双时相QuickBird高分辨率遥感影像进行建筑物变化检测试验，结果表明：本文提出的方法能够削弱光照、观测角度等环境差异对建筑物变化检测的影响，显著改善建筑物变化的检测精度。

关键词: 建筑物变化检测, 信息融合, 随机森林, 影像分割, 高空间分辨率

Abstract: Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remote sensing technology,detecting building changes from high-resolution remote sensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remote sensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remote sensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remote sensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

Key words: building change detection, information fusion, random forest, image segmentation, high resolution

中图分类号:

P237

张志强, 张新长, 辛秦川, 杨晓羚. 结合像元级和目标级的高分辨率遥感影像建筑物变化检测[J]. 测绘学报, 2018, 47(1): 102-112.

ZHANG Zhiqiang, ZHANG Xinchang, XIN Qinchuan, YANG Xiaoling. Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(1): 102-112.

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结合像元级和目标级的高分辨率遥感影像建筑物变化检测

Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

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