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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:: A SUPER RESOLUTION RECONSTRUCTION ALGORITHM TO MULTI-TEMPORAL REMOTE SENSING IMAGES. Pingxiang Li, Jixian Zhang, Huanfeng Shen, Liangpei Zhang

Document type:: Monograph

Structure type:: Chapter

Contents

Table of contents

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

58 
A SUPER RESOLUTION RECONSTRUCTION ALGORITHM TO MULTI-TEMPORAL 
REMOTE SENSING IMAGES 
Pingxiang Li a, *, Jixian Zhang b, Huanfeng Shen c, Liangpei Zhang a 
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 
University, Wuhan, Hubei, China, 430079-pxli@lmars.whu.edu.cn, zlp62@public.wh.hb.cn. 
b Chinese Academy of Surveying and Mapping- zhangjx@casm.ac.cn 
c School of Resource and Environmental Science, Wuhan University-shenhf@whu.edu.cn 
KEY WORDS: Super resolution, Remote Sensing, Multi-temporal images, Registration, Joint framework, Maximum a posterior. 
ABSTRACT: 
In this paper, we propose a super resolution (SR) image reconstruction algorithm to multi-temporal remote sensing images. The aim 
is to reconstruct a high resolution (HR) image by fusing the non-redundant information among the low resolution (LR) remote 
sensing images which captured on different dates by the same sensor. To increase the robustness of the image registration and SR 
reconstruction, we combine the two processes together using the maximum a posterior (MAP) framework. In the solution procedure, 
a cyclic optimization method is employed to solve the desired SR image, registration parameters and outliers which strongly depart 
away from the observation model. We test the proposed algorithm using real multi-temporal MODIS images. The experimental 
results and comparative analyses verily the effectiveness of this algorithm. 
gk = 
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estimation) and Si 
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al. 2007), and a 
implement the im< 
joint framework. I 
parameter vector 
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posteriori (MAP) 
registration paramc 
Z,n 
Applying Bayes’ ru 
z,m = arg 
1. INTRODUCTION 
Image fusion is the combination of two or more different 
images to form a new image by using a certain algorithm. In the 
field of remote sensing, image fusion technique is commonly 
employed for sharpening low resolution (LR) images using one 
or more high resolution (HR) images, such as the fusion of 
multi-spectral images with SAR or panchromatic image. As a 
special case of image fusion, super resolution (SR) image 
reconstruction refers to a process that produces a HR image 
from a sequence of LR images using the non-redundant 
information among them. Generally, the non-redundant 
information roots in the sub-pixel displacements among the 
observed images. 
The multi-frame SR problem was first formulated by Tsai and 
Huang (Tsai and Huang 1984) in the frequency domain using 
discrete Fourier transform (DCT) . Consequently, many kinds 
of spatial domain approaches have been developed. Typical 
spatial domain approaches include non-uniform interpolation 
(Ur and Gross 1992), iterative back projection (IBP) (Irani and 
Peleg 1991), projection onto convex sets (POCS) (Stark and 
Oskoui 1989; Tekalp, Ozkan et al. 1992; Patti, Sezan et al. 1994; 
Patti, Sezan et al. 1997), maximum likelihood (ML) (Tom and 
Katsaggelos 1994), maximum a posteriori (MAP) (Schultz and 
Stevenson 1996) (Hardie, Tuinstra et al. 1997), hybrid ML 
/MAP/POCS (Elad and Feuer 1997), and adaptive filtering 
(Elad and Feuer 1999). 
Although the SR technique has been greatly developed in the 
last decades, it has been applied to generic camera images, 
medical images and video sequence much more commonly than 
remote sensing images. Although some papers in the literature 
provided the SR results of satellite images, most of them used 
synthetic images and assumed known motion parameters. Shen 
et al. (Shen, Ng et al. 2007, accepted) proposed a super 
resolution image reconstruction algorithm to multi-temporal 
remote sensing images. They implemented image registration 
and SR reconstruction separately. This paper extends the 
previous method by employing a joint MAP framework to 
simultaneously estimate the image registration parameters and 
the HR image. This algorithm reinforces the interdependence 
among the motion estimates and HR image in a mutually 
beneficial manner. 
2. IMAGE OBSERVATION MODEL 
For the SR problem, a typical image observation model 
assumes that imaging process involves warping followed by 
blurring and down-sampling to generate LR images from a HR 
image. Let us denote the underlying HR image in vector form 
by Z- [z x ,Z 2 ....,Z LyN ^ LiNi f , where L x N x xL 2 N 2 is the HR 
image size. Letting Lj and L 2 denote the down-sampling 
factors in the horizontal and vertical directions respectively, 
each observed LR image is of size A, x N 2 • Thus, the LR image 
can be represented as g k =[g kA ,g kX ...,g k ^ N J < 
where k = 1,2,.... P, with P being the number of the LR images. 
The typical image observation model can be represented as 
(Elad and Feuer 1997; Park, Park et al. 2003): 
g k =DB k M k z + n k (1) 
where M k is the warp matrix, B k represents the blur 
matrix, D is a down-sampling matrix, and n k represents the 
noise vector. 
In remote sensing imaging, the sun zenith angle and 
atmospheric absorption and scattering affect the amount of 
radiance received by the sensor. These effects in many cases 
can be simply modeled as a linear system (Conel 1985; Roberts, 
Yamaguchi et al. 1985), with which the following image 
observation model can be obtained 
* Corresponding author. 
= ari 
Since p(z,m\g) h 
rewritten as 
Z,m = argm 
Z, 
Assuming the mode 
and variance a 2 , th 
be given by 
P(gi\z,nh) = TT e 
where O k is a diag 
outliers that strong 
order to preserve 
reconstructed imag 
for p{z). The Hube 
Stevenson 1996) 
p(z)= l 
where C is a const 
clique within the 
d c ( z x ,y) is a spati 
often formed by 
and p(-) is the poter 
Pi 
where p is a thresh 
linear regions (S

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