国产HJ小卫星遥感影像多特征融合用于日本海啸灾情快速监测
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摘要
针对特大地震和海啸灾害的特点与灾情监测的需求,设计和构建了一种基于多特征决策级融合的灾情快速变化检测方法,首先利用遥感影像中提取的NDVI、主成分变换分量、独立成分变换分量等特征分别提取变化信息,然后对多种变化信息进行决策级融合,获得具有更高可靠性的变化图,用于灾情分析。将所设计的方法用于国产环境与灾害监测预报小卫星HJ-1A/B数据处理分析,对日本东部沿海区域海啸灾害前后进行变化检测与灾情信息提取试验,有效地检测了海啸灾害后的海水倒灌区域、陆地积水区、植被淹没区以及建筑受损区等变化区域。研究表明,基于多源特征融合的非监督变化检测流程可以快速、有效地提取海啸受灾区域,为灾害应急响应与灾情评估提供支持。
The huge tsunami triggered by an earthquake of magnitude 9.0 on March 11,2011 hit the east coast of Honshu,Japan,and caused serious social and economic losses.According to the characteristics of earthquake and tsunami disaster and the requirements of damage monitoring,a rapid disaster development detection process based on a decision level fusion of multiple features is designed.In this approach,each feature extracted from the original remote sensing images,including NDVI,NDWI,components of the principal component analysis and the independent component analysis,is used to derive a specific change map,and different change maps are then integrated by a decision level fusion algorithm to generate a synthetic change map with a higher reliability,which can be used for the damage assessment.Multi-temporal HJ-1A/B(environment and disaster monitoring and forecasting of small satellite constellation) images are processed by the proposed approach and used for detecting the devastated areas in east coast of Japan before and after tsunami.The experimental results confirm the feasibility and effectiveness of the proposed approach,and demonstrate the advantages of HJ-1A/B remote sensing data.This unsupervised change detection process can identify the tsunami-devastated regions quickly and efficiently,and provide the technical and decision support for the disaster emergency response and loss evaluation.
The huge tsunami triggered by an earthquake of magnitude 9.0 on March 11,2011 hit the east coast of Honshu,Japan,and caused serious social and economic losses.According to the characteristics of earthquake and tsunami disaster and the requirements of damage monitoring,a rapid disaster development detection process based on a decision level fusion of multiple features is designed.In this approach,each feature extracted from the original remote sensing images,including NDVI,NDWI,components of the principal component analysis and the independent component analysis,is used to derive a specific change map,and different change maps are then integrated by a decision level fusion algorithm to generate a synthetic change map with a higher reliability,which can be used for the damage assessment.Multi-temporal HJ-1A/B(environment and disaster monitoring and forecasting of small satellite constellation) images are processed by the proposed approach and used for detecting the devastated areas in east coast of Japan before and after tsunami.The experimental results confirm the feasibility and effectiveness of the proposed approach,and demonstrate the advantages of HJ-1A/B remote sensing data.This unsupervised change detection process can identify the tsunami-devastated regions quickly and efficiently,and provide the technical and decision support for the disaster emergency response and loss evaluation.
引文
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[2]Liu Y L,Wei C J,Yan S Y,et al.Assessment the losses of tsunami disaster in aceh province of Indonesia by remote sensing[C].26th ACRS2005.Haoni,Vietnam,2005,DST1-2.
[3]Bovolo F,Bruzzone L.A split-based approach to unsupervised change detection in large-size multi-temporal images:application to tsunamidamage assessment[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(6):1658-1670.
[4]Lu D S,Mausel P,Brondi′Zio E,et al.Change detection techniques[J].International Journal of Remote Sensing,2004,25(12):2365-2407.
[5]Singh A.Digital change detection techniques using remotely-sensed data[J].International Journal of Remote Sensing,1989,10(6):989-1003.
[6]Coppin P,Jonckheere I,Nackaerts K,et al.Digital change detection methods in ecosystem monitoring:A review[J].International Journal of Remote Sensing,2004,25(9):1565-1596.
[7]Gong J Y,Sui H G,Ma G R,et al.A review of multi-temporal remote sensing data change detection algorithms[C].The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences.Beijing:2008,Vol.XXXVII.Part B7.757-762.
[8]Bruzzone L,Serpico S B.An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):858-867.
[9]Nemmour H,Chibani Y.Multiple support vector machines for land cover change detection:An application for mapping urban extensions[J].ISPRS Journal of Photogrammetry&Remote Sensing,2006,61(2):125-133.
[10]Woodcock C E,Macomber S A,Pax-Lenney M,et al.Monitoring large areas for forest change using Landsat:Generalization across space,time and Landsat sensors[J].Remote Sensing of Environment,2001,78(1-2):194-203.
[11]范海生,马蔼乃,李京.采用图像差值法提取土地利用变化信息方法——以攀枝花仁和区为例[J].遥感学报,2001,5(1):75-80.Fan Haisheng,Ma Ainai,Li Jing.Journal of Remote Sensing,2001,5(1):75-80.
[12]Bovolo F,Bruzzone L.A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(1):218236.
[13]Celik T.Unsupervised change detection in satellite images using principal component analysis and k-means clustering[J].IEEE Geoscience and Remote Sensing Letters,2009,6(4):772-776.
[14]张永红,赵继承,龙艳,等.基于DMC卫星影像对海啸灾情土地覆盖类型变化的分析[J].遥感学报,2005,9(4):498-502.Zhang Yonghong,Zhao Jicheng,Long Yan,et al.Journal of Remote Sensing,2005,9(4):498-502.
[15]黄诗峰,李琳,徐美,等.2004年印度洋海啸印尼亚齐省灾情遥感监测与分析[J].遥感学报,2005,9(4):503-508.Huang Shifeng,Li Lin,Xu Mei,et al.Journal of Remote Sensing,2005,9(4):503-508.
[16]Sumer E,Celebi F V.Detection of tsunami induced changes from high resolution satellite imagery[C].OCEANS2006-Asia Pacific.Singapore,2006.
[17]Theilen-Willige B.Tsunami hazard in northern Venezuela[J].Science of Tsunami Hazards,2006,25(3):144-159.
[18]Li D R.Remotely sensed images and GIS data fusion for automatic change detection[J].International Journal of Image and Data Fusion,2010,1(1):99-108.
[19]柏延臣,王劲峰.结合多分类器的遥感数据专题分类方法研究[J].遥感学报,2005,9(5):555-563.Bo Yanchen,Wang Jinfeng.Journal of Remote Sensing,2005,9(5):555-563.
[20]Le Hegarat-Mascle S,Bloch I,Vidal-Madjar D.Application of dempster-shafer evidence theory to unsupervised classification in multisource remote sensing[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):1018-1031.
[21]Bazi Y,Bruzzone L,Melgani F.An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):874-887.
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