地面建筑物破坏状态检测的多类互信息量评估——震前光学图像与震后SAR图像的融合
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摘要
利用震前可具备的光学图像与震后卫星SAR图像的信息融合,可检测与评估震灾区地表面建筑物破坏状态。本文根据震前光学图像的建筑物特征参数,通过SAR成像仿真算法,模拟各建筑物SAR图像,根据震后SAR真实图像与SAR仿真图像互信息量的比较,综合利用归一化互信息量、梯度互信息量、区域互信息量及检测与评估建筑物破坏状态。采用2010年海地地震灾前的IKONOS光学图像、灾后COSMO-SkyMed与RADARSAT-2的星载SAR图像,进行震后建筑物破坏状态检测与评估。其结果与震后GeoEye光学图像进行了验证,证明了本文方法的可行性。
Quick and accurate evaluation of the building damages after earthquake in urban area is a critical issue for timely assessing the disaster situation and effectively carrying out rescue mission. Very High Resolution (VHR) optical image is usually a direct way to visually monitor natural disaster occurrence. However, under some serious situations, such as bad weather or difficult geographic conditions, the post-event VHR optical image is not always available over large areas. All day and all weather space-borne Synthetic Aperture Radar (SAR) in operation can provide the reliable and high quality images over a large area. Of course, the relationship between optical image and SAR image is not simply a one-to-one visual correspondence. Microwave scattering and penetration through terrain media, and image speckles of SAR observation with a certain resolution are different from VHR optical imaging. How to make fusion of VHR optical and SAR images under different pre-event and post-event situations can provide a useful and operational approach to make timely and accurate evaluation of earthquake losses.
Quick and accurate evaluation of the building damages after earthquake in urban area is a critical issue for timely assessing the disaster situation and effectively carrying out rescue mission. Very High Resolution (VHR) optical image is usually a direct way to visually monitor natural disaster occurrence. However, under some serious situations, such as bad weather or difficult geographic conditions, the post-event VHR optical image is not always available over large areas. All day and all weather space-borne Synthetic Aperture Radar (SAR) in operation can provide the reliable and high quality images over a large area. Of course, the relationship between optical image and SAR image is not simply a one-to-one visual correspondence. Microwave scattering and penetration through terrain media, and image speckles of SAR observation with a certain resolution are different from VHR optical imaging. How to make fusion of VHR optical and SAR images under different pre-event and post-event situations can provide a useful and operational approach to make timely and accurate evaluation of earthquake losses.
引文
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Chen H M,Varshney P K and Arora M K.2003.Performance of mu-tual information similarity measure for registration of multitem-poral remote sensing images.IEEE Transactions on Geoscienceand Remote Sensing,41(11):2445–2454
陈显毅.2009.图像配准技术及其MATLAB编程实现.北京:电子工业出版社
Chini M,Pierdicca N and Emery W J.2009.Exploiting SAR and VHR optical images to quantify damage caused by the2003Bam earthquake.IEEE Transactions Geoscience and Remote Sensing,47(1):145–152
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Jin Y Q and Wang D F.2009.Automatic detection of terrain surface changes after Wenchuan earthquake,May2008from ALOS SAR images using2EM-MRF method.IEEE Geoscience and Remote Sensing Letters,6(2):344–348
Mura M D,Benediktsson J A,Bovolo F and Bruzzone L.2008.An unsupervised technique based on morphological filters for change detection in very high resolution images.IEEE Geo-science and Remote Sensing Letters,5(3):433–437
Pluim J P W,Maintz J B A and Viergever MA.2000.Image regis-tration by maximization of combined mutual information and gradient information.IEEE Transactions on Medical Imaging,19(8):809–814
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Rignot E J M and van Zyl J J.1993.Change detection techniques for ERS-1SAR data.IEEE Transactions on Geoscience and Remote Sensing,31(4):896–906
Russakoff D B,Tomasi C,Rohlfing T and Maturer C R.2004.Image similarity using mutual information of regions//Proceedings of the8th European Conference on Computer Vision(ECCV).Prague:Springer:596–607
Sarndal C E.1974.A comparative study of association measures.Psychometrika,39(2):165–187
Shannon C E.1948.A mathematical theory of communication.Bell System Technical Journal,27:379–423,623–656
Suri S and Reinartz P.2010.Mutual-information-based registration of TerraSAR-X and Ikonos imagery in urban areas.IEEE Trans-actions on Geoscience and Remote Sensing,48(2):939–949
Xu F and Jin Y Q.2006.Imaging simulation of polarimetric SAR for a comprehensive terrain scene using the mapping and projection algorithm.IEEE Transactions on Geoscience and Remote Sens-ing,44(11):3219–3234
Brunner D,Lemoine G and Bruzzone L.2010.Earthquake damage as-sessment of buildings using VHR optical and SAR imagery.IEEE Transactions on Geoscience and Remote Sensing,48(5):2403–2420
Chen H M,Varshney P K and Arora M K.2003.Performance of mu-tual information similarity measure for registration of multitem-poral remote sensing images.IEEE Transactions on Geoscienceand Remote Sensing,41(11):2445–2454
陈显毅.2009.图像配准技术及其MATLAB编程实现.北京:电子工业出版社
Chini M,Pierdicca N and Emery W J.2009.Exploiting SAR and VHR optical images to quantify damage caused by the2003Bam earthquake.IEEE Transactions Geoscience and Remote Sensing,47(1):145–152
Hartley R V L.1928.Transmission of information.Bell SystemTechnical Journal,7:535–563
Jin Y Q and Wang D F.2009.Automatic detection of terrain surface changes after Wenchuan earthquake,May2008from ALOS SAR images using2EM-MRF method.IEEE Geoscience and Remote Sensing Letters,6(2):344–348
Mura M D,Benediktsson J A,Bovolo F and Bruzzone L.2008.An unsupervised technique based on morphological filters for change detection in very high resolution images.IEEE Geo-science and Remote Sensing Letters,5(3):433–437
Pluim J P W,Maintz J B A and Viergever MA.2000.Image regis-tration by maximization of combined mutual information and gradient information.IEEE Transactions on Medical Imaging,19(8):809–814
Pluim J P W,Maintz J B A and Viergever M A.2003.Mutual-infor-mation-based registration of medical images:a survey.IEEE Transactions on Medical Imaging,22(8):986–1004
Rignot E J M and van Zyl J J.1993.Change detection techniques for ERS-1SAR data.IEEE Transactions on Geoscience and Remote Sensing,31(4):896–906
Russakoff D B,Tomasi C,Rohlfing T and Maturer C R.2004.Image similarity using mutual information of regions//Proceedings of the8th European Conference on Computer Vision(ECCV).Prague:Springer:596–607
Sarndal C E.1974.A comparative study of association measures.Psychometrika,39(2):165–187
Shannon C E.1948.A mathematical theory of communication.Bell System Technical Journal,27:379–423,623–656
Suri S and Reinartz P.2010.Mutual-information-based registration of TerraSAR-X and Ikonos imagery in urban areas.IEEE Trans-actions on Geoscience and Remote Sensing,48(2):939–949
Xu F and Jin Y Q.2006.Imaging simulation of polarimetric SAR for a comprehensive terrain scene using the mapping and projection algorithm.IEEE Transactions on Geoscience and Remote Sens-ing,44(11):3219–3234
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