智能摄影测量和图像处理在高分辨率光学遥感影像处理中的应用——CRC-AGIP实验室的案例

doi:10.11947/j.AGCS.2018.20180011

测绘学报 ›› 2018, Vol. 47 ›› Issue (6): 722-729.doi: 10.11947/j.AGCS.2018.20180011

• 基于机器视觉的数字摄影测量的新理论新方法 • 上一篇下一篇

智能摄影测量和图像处理在高分辨率光学遥感影像处理中的应用——CRC-AGIP实验室的案例

Yun ZHANG

新不伦瑞克大学大地测量与地球空间信息工程系, 加拿大高级地球空间信息图像处理实验室, 加拿大新不伦瑞克省, 弗雷德里克顿, E3B5A3

收稿日期:2017-12-06 修回日期:2018-03-22 出版日期:2018-06-20 发布日期:2018-06-21
作者简介:Yun ZHANG(1959-),男,博士,教授,加拿大工程院院士,加拿大首席研究教授,研究方向为遥感影像处理、计算机视觉、摄影测量。E-mail:YunZhang@UNB.ca
基金资助:
加拿大自然科学和工程研究基金委员会（NSERC）发现资助计划（Discovery Grant Program）（2001-PIN236406；2006-PIN236406；2011-PIN236406）；NSERC研究工具与仪器资助计划（2003-PIN236406）；加拿大创新基金委员会（CFI） John R Evans领导者基金（2008-950-206422；2014-950-228916）

Smart Photogrammetric and Remote Sensing Image Processing for Optical Very High Resolution Images-Examples from the CRC-AGIP Lab at UNB

Yun ZHANG

Canada Advanced Geomatics Image Processing Laboratory, Department of Geodesy and Geomatics Engineering(GGE), University of New Brunswick(UNB), Fredericton New Brunswick E3B5A3, Canada

Received:2017-12-06 Revised:2018-03-22 Online:2018-06-20 Published:2018-06-21
Supported by:
The Natural Sciences and Engineering Research Council of Canada (NSERC),Discovery Grants (Nos.2001-PIN236406;2006-PIN236406;2011-PIN236406);The NSERC Research Tools and Instruments Grants (No.2003-PIN236406);The Canada Foundation for Innovation (CFI),John R.Evans Leaders Fund (Nos.2008-950-206422;2014-950-228916)

摘要/Abstract

摘要： 本文介绍了加拿大新不伦瑞克大学（UNB）高级地球空间信息图像处理实验室（CRC-AGIP实验室）和大地测量与地球空间信息工程系（GGE）开发的一些影像处理技术。通过创新性地利用高分辨率遥感光学影像的各种特性，这些技术解决了一些摄影测量和遥感中的重要问题并实现了一些创造新的应用。所介绍的技术包括：自动影像融合（UNB-PanSharp）、卫星影像在线制图、街景技术、单景卫星影像移动车辆检测、有监督影像分割、平滑区域影像匹配及不同视角影像变化检测。

关键词: 遥感, 光学影像, 高分辨率, Pan锐化, 在线制图, 街景, 移动信息检测, 影像分割, 影像匹配, 变化检测

Abstract: This paper introduces some of the image processing techniques developed in the Canada Research Chair in Advanced Geomatics Image Processing Laboratory (CRC-AGIP Lab) and in the Department of Geodesy and Geomatics Engineering (GGE) at the University of New Brunswick (UNB),Canada.The techniques were developed by innovatively utilizing the characteristics of the available very high resolution remote sensing optical images to solve important problems or create new applications in photogrammetry and remote sensing.The techniques to be introduced are:automated image fusion (UNB-PanSharp),satellite image online mapping,street view technology,moving vehicle detection using single set satellite imagery,supervised image segmentation,image matching in smooth areas,and change detection using images from different viewing angles.

Key words: remote sensing, optical image, high resolution, pan-sharpening, online mapping, street view, moving information detection, image segmentation, image matching, change detection

中图分类号:

P237

Yun ZHANG. 智能摄影测量和图像处理在高分辨率光学遥感影像处理中的应用——CRC-AGIP实验室的案例[J]. 测绘学报, 2018, 47(6): 722-729.

Yun ZHANG. Smart Photogrammetric and Remote Sensing Image Processing for Optical Very High Resolution Images-Examples from the CRC-AGIP Lab at UNB[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(6): 722-729.

参考文献

[1] SCHENK T.Introduction to Photogrammetry[G].Neil Ave,Columbus,OH:The Ohio State University,2005.
[2] GRUEN A.Digital Photogrammetric Imaging-Past,Present and Future[R].Federal:The Swiss Federal Institutes of Technology (ETH),2009.
[3] BAUMANN P R.History of Remote Sensing,Satellite Imagery,Part Ⅱ[G].New York,NY:State University of New York,2009.
[4] ZHANG Yun.Ten years of Technology Advancement in Remote Sensing and The Research in the CRC-AGIP Lab in GGE[J].Geomatica,2010,64(2):173-189.
[5] ZHANG Y.Highlight Article:Understanding Image Fusion[J].Photogrammetric Engineering and Remote Sensing,2004,70(6):657-661.
[6] ZHANG Yun,MISHRA R.From UNB PanSharp to Fuze Go-the Success Behind the Pansharpening Algorithm[J].International Journal of Image and Data Fusion,2014,5(1):39-53.
[7] ZHANG Yun,ROSHAN A,JABARI S,et al.Understanding the Quality of Pansharpening:A Lab Study[J].Photogrammetric Engineering & Remote Sensing,2016,82(10):747-755.
[8] RAWLINSON S,LEE Y C,ZHANG Y.Development of a Software Prototype for the Geo-referencing and Visualization of Non-metric TERRESTRIAL Photography in a GIS Environment[C]//Proceedings of the 23rh to 27th ASPRS 2001 Annual Conference.St.Louis,Missouri:ASPRS,2001.
[9] LEVITZ S.Putting the North on the Map:Google Recruits Northerners for Help with Maps[M].[S.l.]:The Canadian Press,2012.
[10] GANNES L.Ten Years of Google Maps,From Slashdot to Ground Truth[EB/OL].[2015-02-08].https://www.recode.net/2015/2/8/11558788/ten-years-of-google-maps-from-slashdot-to-ground-truth.
[11] XIONG Zhen,ZHANG Yun.A Novel Interest Point Matching Algorithm for High Resolution Satellite Images[J].IEEE Transactions on Geoscience and Remote Sensing,2009,47(12):4189-4200.
[12] XIONG Zhen,ZHANG Yun.An Initial Study on Vehicle Information Extraction from Single Pass of Satellite QuickBird Imagery[J].Photogrammetric Engineering and Remote Sensing,2008,74(11):1401-1412.
[13] ZHANG Yun,MAXWELL T.A Fuzzy Logic Approach to Optimization of Segmentation of Object-Oriented Classification[C]//Proceedings of SPIE 50th Annual Meeting-Optics & Photonics 2005.San Diego,California,USA:SPIE,2005.
[14] TONG Hengjian,MAXWELL T,ZHANG Yun,et al.A Supervised and Fuzzy-based Approach to Determine Optimal Multi-resolution Image Segmentation Parameters[J].Photogrammetric Engineering & Remote Sensing,2012,78(10):1029-1044.
[15] JABARI S,ZHANG Yun.RPC-Based Coregistration of VHR Imagery for Urban Change Detection[J].Photogrammetric Engineering & Remote Sensing,2016,82(7):521-534.

智能摄影测量和图像处理在高分辨率光学遥感影像处理中的应用——CRC-AGIP实验室的案例

Smart Photogrammetric and Remote Sensing Image Processing for Optical Very High Resolution Images-Examples from the CRC-AGIP Lab at UNB

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	季顺平, 刘瑾, 高建, 龚健雅. 多视影像深度学习密集匹配三维重建智能框架[J]. 测绘学报, 2025, 54(9): 1633-1646.
[2]	闫凯, 徐健明, 王桥. 地表异常遥感探测轻量化大模型方法[J]. 测绘学报, 2025, 54(9): 1664-1676.
[3]	张继贤, 顾海燕, 倪欢, 李海涛, 杨懿, 丁少鹏, 隋淞蔓. 遥感智能变化检测的深度学习方法：演变与发展趋势[J]. 测绘学报, 2025, 54(8): 1347-1370.
[4]	吴田军, 李曼嘉, 骆剑承, 李子琪, 胡晓东, 郜丽静, 沈占锋. 耦合空间分布模式的复杂山区地块作物遥感分类方法[J]. 测绘学报, 2025, 54(7): 1215-1229.
[5]	谢亚坤, 赵耀纪, 涂佳星, 夏瑞丰, 冯德俊, 刘苏凝, 陈虹宇, 朱军. 融合边缘与全局特征的遥感影像显著性目标检测方法[J]. 测绘学报, 2025, 54(7): 1265-1279.
[6]	董子博, 王竞雪, 卜丽静, 房琳, 许峥辉. MAFNet：基于多尺度空洞融合网络的遥感影像建筑物提取方法[J]. 测绘学报, 2025, 54(6): 1094-1106.
[7]	李海峰, 郭旺, 吴梦伟, 彭程里, 朱庆, 刘瑜, 陶超. 视觉-语言联合的遥感地物概念表达与智能解译：原理、挑战与机遇[J]. 测绘学报, 2025, 54(5): 853-872.
[8]	王超, 陈天宇, 张同, AhmedTanvir, 纪立强, 谢涛, 杨佳俊, 王帅. 基于全局差分增强模块和平衡惩罚损失的多源光学遥感影像变化检测[J]. 测绘学报, 2025, 54(5): 873-887.
[9]	赵一鸣, 胡克林, 涂可龙, 卿雅娴, 杨超, 祁昆仑, 吴华意. 基于SAR与光学遥感影像融合的多标签场景分类方法[J]. 测绘学报, 2025, 54(5): 911-923.
[10]	张新长, 齐霁, 陶超, 傅思扬, 郭明宁, 阮永检. 光学遥感影像去云研究进展、挑战与趋势[J]. 测绘学报, 2025, 54(4): 603-620.
[11]	高永刚, 刘雨婷, 徐涵秋. 基于Landsat长时间序列影像的城市生态品质时空演变及驱动因素分析[J]. 测绘学报, 2025, 54(3): 510-522.
[12]	侯昭阳, 闫浩文, 张黎明, 马荣娟, 屈睿涛. 基于耦合神经P系统与区块链的遥感影像零水印版权保护方法[J]. 测绘学报, 2025, 54(12): 2247-2261.
[13]	武昊, 侯东阳, 张俊, 张平, 刘玉轩, 杜磊, 康路, 程滔, 陈军. 动态服务计算支持的自然资源遥感监测监管平台关键技术研究[J]. 测绘学报, 2025, 54(11): 1992-2008.
[14]	龚希, 陈占龙, 郑恒强, 胡胜, 张洪艳. 融合迁移特征空间和语义信息的遥感影像场景分类方法[J]. 测绘学报, 2025, 54(11): 2009-2025.
[15]	张志力, 姜慧伟, 胡翔云. 面向极简交互的遥感地物精确批量提取框架[J]. 测绘学报, 2025, 54(10): 1863-1876.