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Mapping without the sun

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fullscreen: Mapping without the sun

Monograph

Persistent identifier:: 856578517

Author:: Zhang, Jixian

Title:: Mapping without the sun

Sub title:: techniques and applications of optical and SAR imagery fusion ; Chengdu, China, 25 - 27 September 2007

Scope:: 1 Online-Ressource (III, 352 Seiten)

Year of publication:: 2007

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856578517

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: AUTOMATED VEHICLE INFORMATION EXTRACTION FROM ONE PASS OF QUICKBIRD IMAGERY. Zhen Xiong, Yun Zhang

Document type:: Monograph

Structure type:: Chapter

Mapping without the sun
Cover
ColorChart
Title page
Table of Content
Foreword
Scientific Committee:
Organizing Committee:
DECISION FUSION OF MULTITEMPORAL SAR AND MULTISPECTRAL IMAGERY FOR IMPROVED LAND COVER CLASSIFICATION B. Waske a, J. A. Benediktsson b’*
SYNERGISTIC USE OF OPTICAL AND INSAR DATA FOR URBAN IMPERVIOUS SURFACE MAPPING: A CASE STUDY IN HONG KONG. Liming Jiang, Hui Lin, Mingsheng Liao, Limin Yang
A NOVEL FUSION METHOD OF SAR AND OPTICAL IMAGES FOR URBAN OBJECT EXTRACTION. Jia Yonghong, Rick S. Blum,Ma Yunxia
REAL-TIME SAR SIMULATION FOR CHANGE DETECTION APPLICATIONS BASED ON DATA FUSION. Timo Balz
THE OPTIMIZING METHOD OF FUSING SAR WITH OPTICAL IMAGES FOR INFORMATION EXTRACTION. Feng Xie, Yingying Chen, Yi Lin
ORTHORECTIFYING SPACEBORNE SAR BY DEM BASED ON FINE REGISTRATION. Hongjian You, Fu Kun
DETECTION AND ANALYSIS OF EARTHQUAKE-INDUCED URBAN DISASTER BASED ON INSAR COHERENCE. M. He, X. F. He
MULTI-SCALE SAR LAND USE/LAND COVER CLASSIFICATION BASED ON CO-OCCURRENCE PROBABILITIES. Yu ZENG, Jixian ZHANG, J. L.VAN GENDEREN, Haitao LI
TERRASAR-X AND TANDEM-X: REVOLUTION IN SPACEBORNE RADAR. Ralf Duering
A MULTI-WAVELENGTH IMAGING SYSTEM FOR DETECTION OF FOREIGN FIBERS IN COTTON. Lu Dehao
A FUSION ALGORITHM OF HIGH SPATIAL AND SPECTRAL RESOLUTION IMAGES BASED ON ICA. GuoKun Zhang, LeiGuang Wang, Hongyan Zhang
A SUPER RESOLUTION RECONSTRUCTION ALGORITHM TO MULTI-TEMPORAL REMOTE SENSING IMAGES. Pingxiang Li, Jixian Zhang, Huanfeng Shen, Liangpei Zhang
COMPARISON OF MORPHOLOGICAL PYRAMID AND LAPLACIAN PYRAMID TECHNIQUES FOR FUSING DIFFERENT FOCUSING IMAGES. Jia Yonghong, Fu Xiujun, Yu Hongwei
MONITORING AND CHARACTERIZING NATURAL HAZARDS WITH SATELLITE INSAR IMAGERY. Z. Lu
PREDICTION AND SIMULATIONS OF MALAYSIAN FOREST FIRES BY MEANS OF RANDOM SPREAD. Jean Serra, Mohd Dini Hairi Suliman, and Mastura Mahmud
TEXTURE CLASSIFICATION RESEARCH BASED ON LIFTING-BASED DWT 9/7 WAVELET. Hong Zhang, Ning Shu
REMOTE SENSING IMAGE SEGMENTATION BASED SELF-ORGANIZING MAP AT MULTI-SCALE. Zhao Xi-an, Zhang Xue-wen Wei Shi-yan
A JOINT SPATIAL-TEMPORAL CLASSIFICATION AND FEATURE BOUNDARY UPDATING MODEL. P. Caccetta
THE APPLICATION RESEARCH IN ASSISTANT CLASSIFICATION OF REMOTE SENSING IMAGE BY TEXTURE FEATURES COMBINED WITH SPECTRA FEATURES. Y. M. Fang, X. Q. Zuo, Y. J. Yang, J. H. Feng
A KIND OF THE METHODS FOR SAR AND OPTICAL IMAGES FUSION BASED ON THE LIFTING WAVELET. Shao Yongshe, Chen Ying, Li Jing
SOIL MOISTURE RETRIEVAL COMBINING OPTICAL AND RADAR DATA DURING SMEX02. Chen Quan, Li Zhen, Tian Bangsen
A TARGET DETECTION METHOD BASED ON SAR AND OPTICAL IMAGE DATA FUSION. Sun Mu-han, Zhou Yin-qing, Xu Hua-ping
FUSION SAR AND OPTICAL IMAGES TO DETECT OBJECT-SPECIFIC CHANGES. Mu H. Wang, Hai T. Li, Ji. X Zhang ,Jing H. Yang
APPLICATION OF DINSAR AND GIS FOR UNDERGROUND MINE SUBSIDENCE MONITORING. YAN Ming-xing, MIAO Fang, WANG Bao-cun, QI Xiao-ying
THE DETECTION OF SUBSIDENCE AT PERMANENT FROZEN AREA IN QINGHAI-TIBETAN PLATEAU. Z. Li, C. Xie, Q. Chen
RESEARCH ON SURFACE SUBSIDENCE MONITORING WITH INSAR/GPS DATA FUSION IN MINING AREA. ZHANG Ji-chao, SONG Wei-dong, ZHANG Ji-xian, SHI Jin-feng
SEVEN YEARS OF MINING SUBSIDENCE DETECTED BY D-InSAR TECHNIQUE IN FUSHUN CITY, CHINA. Y. L. Chen, X. L. Ding, C. Huang, Z. W. Li
A METHOD ON HIGH-PRECISION RECTIFICATION AND REGISTRATION OF MULTI-SOURCE REMOTE SENSING IMAGERY. Bin Liu, Guo Zhang, Xiaoyong Zhu, Jianya Gong
STUDY ON TIE POINT SELECTION FOR CO-REGISTRATION OF DIFFERENT RESOLUTION IMAGERY. Zhen Xiong, Yun Zhang
THE STUDY OF SPACE INTERSECTION MODEL BASED ON DIFFERENT-SOURCE HIGH RESOLUTION RS IMAGERY. Weixi Wang, Qing Zhu
AN OPTIMIZATION HIGH-PRECISION REGISTRATION METHOD OF MULTI-SOURCE REMOTE SENSING IMAGES. LIN Yi, JIAN Jianfeng , ZHANG Shaoming, XIE Feng
A METHODOLOGY OF LUCC CHANGE DETECTION BASED ON LAND USE SEGMENT. Ning Shu, Hong Zhang, Xue Li, Yan Wang
APPLICATION OF MULTI-TEMPORAL TM (ETM+) IMAGE IN MONITORING MINING ACTIVITIES AND RELATED ENVIRONMENT CHANGES: A CASE STUDY AT DAYE, HUBEI, CHINA. Shiyong YU, Zhihua CHEN, Yanxin WANG
LAND COVER CHANGE AND CLIMATIC VICISSITUDE RESEARCH IN HEADSTREAM REGIONOF YELLOW RIVER IN THE NINETIES OF THE TWENTIETH CENTURY. DAI Ji-guang, YANG Tai-bao, REN Jia-qiang
LAND USE CHANGES IN THREE GORGES RESERVOIR AREA IN RECENT 30 YEARS. Sun xiaoxia, Zhang jixian, Liu zhengjun
AUTOMATED VEHICLE INFORMATION EXTRACTION FROM ONE PASS OF QUICKBIRD IMAGERY. Zhen Xiong, Yun Zhang
CLASSIFICATION OF LAND TYPES IN MINERAL AREAS BASED ON CART. Wenbo Wu, Yuping Chen, Jiaojiao Meng, Tingjun Kang
OBJECT-ORIENTED CLASSIFICATION OF HIGH-RESOLUTION REMOTE SENSING IMAGERY BASED ON MRF AND SVM. GU Haiyan, LI Haitao, ZHANG feng, HAN Yanshun, YANG Jinghui
EXTENSIBLE LAND USE AND LAND COVER CLASSIFICATION FRAMEWORK DESIGN BASED ON REMOTELY SENSED DATA. Wang Juanle
THE ROAD EXTRACTION IN THE AREA COVERED WITH HIGH VEGETATION USING THE FUSION IMAGE OF SAR AND TM. Shen Jin-li, Yu Wu-yi, Qi Xiao-ping, Zhang Yi-min
DISCRETE WAVELET-BASED FUSION OF TM MULTI-SPECTRAL IMAGE AND SAR IMAGE DATA. Liang Shouzhen, Li Lanyong
FUSING SAR AND OPTICAL IMAGES BASED ON COMPLEX WAVELET TRANSFORM. Shuai Xing, Qing Xu
A COMPREHENSIVE QUALITY EVALUATION METHOD OF INFORMATION FUSION FROM HIGH-RESOLUTION AIRBORNE SAR AND SPOT5 IMAGES. Wenqing Dong, Qin Yan,
A SIMPLIFIED FUSION METHOD BASED ON SYNTHETIC VARIABLE RATIO. Pang Xinhua, Xi Bin, Chen Luyao, Pan Yaozhong,, Zhuang Wei
A NOVEL IMAGE FUSION METHOD BASED ON 2DPCA IN REMOTE SENSING. Xue-ming Wu, Wu-nian Yang
A METHOD TO DETERMINE SPATIAL RESOLUTION OF REMOTE SENSING FUSED IMAGE QUANTITATIVELY. X. J. Yue, L. Yan, G. M. Huang
A NEW PAN-SHARPENING ALGORITHM AND ITS APPLICATION IN GEOGRAPHIC FEATURES INFORMATION EXTRACTION. ZHU Lijiang
RESEARCH ON THE PROCESS OF LAND USE/COVER CHANGE IN THREE GORGES RESERVOIR AREA IN RECENT 30 YEARS. SHAO Huai-Yong, XIAN Wei, LIU Xue-Mei, YANG Wu-Nian
THE STUDY OF LAND USE CHANGE DETECTION BASED ON SOLE PERIOD RS IMAGE. Song Weidong, Wang Jingxue, Qin Yong
ANALYSIS OF THE LAND USE OF SHENYANG MINING DISTRICT AND ITS DRIVING FORCE. Kaixuan Zhang, Wenbo Wu, Chongchang Wang, Tingjun Kang
REMOTE-SENSING IMAGE COMPRESSION BASED ON FRACTAL THEORY. Chao Mu, Qin Yan, Jie Yu, Huiling Qin
MATRIX DECOMPOSITION AND MATRIX SOLVERS IN PHOTOGRAMMETRY. Cheng Chunquan, Deng Kazhong, Zhang Jixian, YanQin
INVESTIGATING SEVERAL POINT CLOUD REGISTRATION MOTHEDS. Luo Dean, Zhou Keqin, Huang Jizhong
THE ACCURACY ASSESSMENT OF ORTHORECTIFIED ASTER IMAGE. Li Baipeng, Yan Qin, Chen Chunquan
EPIPOLAR RESAMPLING OF DIFFERENT TYPES OF SATELLITE IMAGERY. Jiaying Liu, Guo Zhang, Deren Li
REFINEMENT AND EVALUATION OF BEIJING-1 ORTHORECTIFICATION BASED ON RFM. Jianming Gong, Xiaomei Yang, Chenghu Zhou, Xiaoyu Sun, Cunjin Xue
LAND COVER CLASSIFICATION BY IMPROVED FUZZY C-MEAN CLASSIFIER. ZHAO Quan-hua, SONG Wei-dong, Bao Yong
RESEARCH ON GRIDDING PROCESSING STRATEGIES OF REMOTE SENSING IMAGE SEGMENTATION BY REGION GROWTH. ZHU Hong-chun, ZHANG Ji-xian, LI Hai-tao, YANG Jing-hui, LIU Hai-ying
TEXTURE ANALYSIS IN INFORMATION EXTRACT IN THE HIGH RESOLUTION RS IMAGES LU Shuqiang
THE STUDY OF REMOTE SENSING IMAGE INFORMATION EXTRACTION TECHNIQUES BASED ON KNOWLEDGE. Wenbo Wu, Jiaojiao Meng, Yuping Chen, Jing Chen
A NEW METHOD OF SIMULATION OF INTERFEROGRAM IMAGE FOR REPEAT-PASS SAR SYSTEM. Jianmin Zhou, Zhen Li, Xinwu Li, Chou Xie
COMPARISON AND IMPROVEMENT OF POSITION METHODS OF AIRBORNE STEREO SAR IMAGES. H. D. Fan, K. Z. Deng, G. M.Huang, Z. Zhao., X. J. Yue, X. M. Luo, Y. F. Ling
STUDY ON TOPOGRAPHIC MAP UPDATING WITH HIGH RESOLUTION AIRBORNE SAR IMAGE. X .M. Luo, G. M. Huang, Z. Zhao
AN EXPERIMENT OF HIGH RESOLUTION SAR IMAGE IN DYNAMIC MONITORING THE CHANGE OF CONSTRUCTION LAND. CaoYinxuan, Zhang Yonghong, YanQin, ZhaoZheng
RESEARCH ON STATISTICS AND SPATIAL ANALYSIS OF DRAINAGE BASIN'S IMPORTANT GEOGRAPHICAL ELEMENTS. Liu Ping, Liu Jiping, Zhao Rong
THE RESEARCH AND ESTABLISHMENT OF IMAGE DATABASE SYSTEM BASED ON ORACLE. Li Lanyong, Song Weidong, Chen Zhaoliang, Zhao Hongfeng
SITE SELECTION FOR SATELLITE GEOMETRIC TEST RANGE IN CHINA. Xinxin Zhu, Guo Zhang, Qing Zhu, Xinming Tang
ANALYSIS OF IMAGES GEOMETRIC RECTIFICATION FOR QUICKBIRD. WANG Chong-chang , WANG Li-li, Zhang Li, Zhang Kai-xuan, Ma Zhen-li, ZHANG Zhen-yong
RESEARCH ON DYNAMIC SYMBOL BASE. Yang ping, Tang Xinming, Wang Shengxiao, Lei Bing, Wang Huibing
DETERMINATION OF CHLOROPHYLL CONCENTRATION IN THREE GORGES DAM USING CHRIS/PROBA IMAGE DATA. GAI Li-ya, LIU Zheng-jun,ZHANG Ji-xian
RESEARCH ON LAND SANDY DESERTIFICATION WITH REMOTE SENSING -Take Qinghai Lake Areas as an example. Jian Ji, Chen Yuanyuan, Yang wunian, Tang nengfu
METHODS AND APPLICATION OF QUALITY ASSESSMENT FOR REMOTE SENSING IMAGE COMPRESSION. ZHAI Liang, TANG Xinming, ZHANG Guo, ZHU Xiaoyong
ON-ORBIT MTF ESTIMATION METHODS FOR SATELLITE SENSORS. LI Xianbin, JIANG Xiaoguang, Tang Lingli
AUTHOR INDEX
KEYWORDS INDEX
Cover

Full text

183
AUTOMATED VEHICLE INFORMATION EXTRACTION FROM ONE PASS OF
QUICKBIRD IMAGERY
Zhen Xiong, Yun Zhang
Department of Geodesy & Geomatics Engineering, University of New Brunswick,
15 Dineen Drive, PO Box 4400, Fredericton, NB, Canada E3B 5A3
v009v@unb.ca, yunzhang@unb.ca
Commission VI, WG VI/4
KEY WORDS: Vehicle Information Extraction, One Pass, Quickbird Imagery
ABSTRACT: Vehicle information is useful for transportation management, security surveillance and military applications. The
vehicle information includes vehicle’s ground position, moving speed, and moving direction. Vehicle detection and vehicle
information extraction is usually based on Radar, SAR, or video. The platforms are almost ground based or airborne based; but
seldom is space-borne based. Basically these techniques are usually used for security surveillance or military applications. Some of
them just provide the service of catching vehicle, without any information on the vehicle’s position and velocity. An automated
method of vehicle information extraction from Quickbird imagery is presented in this paper. Because there is a time interval between
Quickbird’s panchromatic and the multi-spectral imagery, once we get a vehicle’s image positions on both the MS and the Pan
imagery, we can calculate vehicle’s ground position and distance the vehicle moved during the time interval, so as to calculate
vehicle’s moving speed and moving direction. In this research, an image classification based vehicle detection method, a region
growing method to refine the image position, a multi-step image matching method to search the corresponding vehicle position, and
an algorithm to calculate vehicle’s ground position from its image position based on DEM are developed. From vehicle’s image
position to ground position, image position, sensor model, DEM, image matching, and region growing are involved in the process. In
this paper, the methodology is introduced first. Later an experiment and result are given. Finally the discussion and conclusion are
delivered.
1. INTRODUCTION
Moving target detection is a fast growing research field. Most
of techniques for moving target detection are based on Radar
[Liu Guoqing, et al., 2001; Nag, S., et al., 2003; Liu, C.-M. Jen,
C.-W., 1992], SAR [Dias, J.M.B., et al., 2003; Hong-Bo Sun, et
al., 2002; Pettersson, M.I., 2004; Soumekh, M.I., 2002], or
video [Munno, C.J., et al., 1993]. The platforms are almost
ground based [Castellano, G., et al., 1999; Nag, S., et al., 2003;
Munno, C.J., et al., 1993; Pettersson, M.I., 2004] or airborne
based [Liu Guoqing, et al., 2001; Hong-Bo Sun, et al., 2002;
Soumekh, M.I., 2002]. But seldom is space-borne based.
Basically these techniques are usually used for security
surveillance or military applications. Some of them just deliver
the service of catching moving target, without any information
on the target’s position and velocity. Based on different
equipment, different techniques were adopted for catching
moving targets, such as using generalized likelihood ratio as a
threshold to decide which target is moving [Liu Guoqing, et al.,
2001; Dias, J.M.B., et al., 2003; Pettersson, M.I., 2004]; using a
filter for digital moving target detection [Nag, S., et al., 2003];
applying a fractional Fourier transformation [Hong-Bo Sun, et
al., 2002]; or utilizing victo’s frequency domain spatiao-
temporal filtering and spatio-temperal constraint error of image
frame pairs to detect and track moving targets (e.g., personnel)
in natural scenes in spite of low image contrast, changes in the
target's infra-red image pattern, sensor noise, or background
clutter [Munno, C.J., et al., 1993].
In this paper we introduce a new technique to vehicle’s
positions and moving speed based on a single set of high spatial
resolution satellite imagery. Such single set of satellite imagery
consists of one panchromatic image and the corresponding
multi-spectral imagery, which are acquired by some current
high resolution satellites, such as SPOT, IKONOS, and
Quickbird. Although the time interval between panchromatic
image and multi-spectral image is very small, if the vehicle is
moving, the panchromatic image and multi-spectral image
should record this position change during satellite time interval.
So theoretically if the vehicle is moving, we can find the
vehicle is moving and even moving speed of the vehicle. We
developed an algorithm to calculate the position and moving
speed of moving vehicle which is acquired by both
panchromatic and multispectral images at a very close time.
2. METHODOLOGY
To date, many satellites can acquire both the panchromatic
(PAN) and the multi-spectral (MS) images at the same time.
But because of technique arrangement, these satellites usually
catch the panchromatic and multi-spectral images not really at
same time. Usually there is a very small time interval between
the PAN and the MS imagery. Therefore, if a ground vehicle is
moving, theoretically this moving vehicle should be record in
different ground position. That is to say, if we can calculate
vehicle’s ground position from image coordinates, we should
obtain two different coordinates from the PAN and the MS
images respectively for the same moving vehicle. Then from
these two ground coordinates, we can calculate the moving
speed and moving direction of the vehicle. This is the elements
of our moving vehicle detection technique.
However, because the time interval between the PAN and the
MS imagery is very small, less than 1 second, the position of
moving vehicle changes within this small time interval is also
very small. Therefore if the error of position calculation is
greater than the value of its position change, we can never
detect moving vehicle correctly. Therefore, some methods must
be used to minimize the errors of vehicle position calculation.
Following is the detailed steps of moving vehicle information
extraction.

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