Damage assessment in urban areas using post-earthquake airborne PolSAR imagery

Zhao, Lingli; Yang, Jie; Li, Pingxiang; Zhang, Liangpei; Shi, Lei; Lang, Fengkai

doi:10.1080/01431161.2013.860566

Cited by 48 publications

(47 citation statements)

References 31 publications

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“…As for the extraction of other classes, such as buildings and grasslands, more features need to be introduced. For instance, texture is a notable characteristic for buildings, which exhibit a great difference with the other land-cover classes [47], and interferometric coherence is an important indicator for distinguishing different vegetation types [48]. Our follow-up research will mainly focus on SAR data fusion by use of more features that are sensitive to the different land-cover types in multi-frequency PolInSAR datasets.…”

Section: Discussionmentioning

confidence: 99%

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Xie

Zhang

et al. 2015

Remote Sensing

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Mapping and monitoring of woodland resources is necessary, since woodland is vital for the natural environment and human survival. The intent of this paper is to propose a fusion scheme for woodland extraction with different frequency (P-and X-band) polarimetric synthetic aperture radar (PolSAR) and interferometric SAR (InSAR) data. In the study area of Hanjietou, China, a supervised complex Wishart classifier based on the initial polarimetric feature analysis was first applied to the PolSAR data and achieved an overall accuracy of 88%. An unsupervised classification based on elevation threshold segmentation was then applied to the InSAR data, with an overall accuracy of 90%. After Dempster-Shafer (D-S) evidence theory fusion processing for the PolSAR and InSAR classification results, the overall accuracy of fusion result reached 95%. It was found the proposed fusion method facilitates the reduction of polarimetric and interferometric SAR classification errors, and is suitable for the extraction of large areas of land cover with a uniform texture and height. The woodland extraction accuracy of the study area was sufficiently high (producer's accuracy of 96% and user's accuracy of 96%) enough that the woodland map generated from the fusion result can meet the demands of forest resource mapping and monitoring.

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Section: Discussionmentioning

confidence: 99%

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Xie

Zhang

et al. 2015

Remote Sensing

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“…Unfortunately, night-time and severe weather often limit the use of optical images in practice [12]. On the other hand, thanks to the unique characteristics of microwaves, SAR sensors can not only acquire periodic images regardless of weather and time, but can also provide valuable information on biophysical and geophysical parameters [13][14][15][16]. Although a number of methods have been proposed for single-channel SAR images [17][18][19][20], the interpretation of the backscattering changes of the land cover is limited [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure

Zhao

Lü

et al. 2017

Remote Sensing

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Accurate and timely change detection of the Earth's surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection includes both unsupervised and supervised methods. Unsupervised change detection is simple and effective, but cannot detect the type of land cover change. Supervised change detection can detect the type of land cover change, but is easily affected and depended by the human interventions. To solve these problems, a novel method of change detection using a joint-classification classifier (JCC) based on a similarity measure is introduced. The similarity measure is obtained by a test statistic and the Kittler and Illingworth (TSKI) minimum-error thresholding algorithm, which is used to automatically control the JCC. The efficiency of the proposed method is demonstrated by the use of bi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method can identify the different types of land cover change and can reduce both the false detection rate and false alarm rate in the change detection.

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“…Guo et al (2012) and Li et al (2012) introduced the circular polarization correlation coefficient ρ and proposed the H-α-ρ method to extract collapsed building information using RADARSAT-2 polarimetric data after the "4.14" Yushu earthquake. Zhao et al (2013) introduced the texture parameter of homogeneity to improve the H-α-ρ method using high-resolution airborne PolSAR data. Shen et al (2015) used the method of image retrieval based on feature template matching to extract the collapsed building information.…”

Section: Introductionmentioning

confidence: 99%

Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake

Zhai

Huang

2016

Earth Planet Sp

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Earthquakes are one of the most destructive natural disasters. Efficiently and quickly acquiring building earthquake damage information can help to reduce the casualties after an earthquake. In this paper, for convenience, speed, and precision, building damage information is extracted using a single post-earthquake PolSAR image. In PolSAR images, the undamaged parallel buildings characterized by double-bounce scattering are different from the collapsed buildings characterized by volume scattering, but the undamaged oriented buildings are very similar to collapsed buildings because of their scattering mechanism ambiguity in the early traditional model-based decomposition. Therefore, the collapsed buildings are difficult to extract accurately. In this paper, the scheme of polarization orientation angle (POA) compensation is employed to enhance the double-bounce scattering power of the oriented buildings, and the difference in the relative contribution change rate of scattering components before and after POA compensation is proposed to further enhance the difference between collapsed buildings and oriented buildings, in order that the collapsed buildings can be extracted more accurately. The "4.14" Yushu earthquake, which occurred in Yushu County, Qinghai province of China, is used as the case study to test the proposed method, and an airborne high-resolution PolSAR image of the urban region of Yushu County is used in the experiment. The experimental results show that the accuracy of building damage information extraction can be improved by the use of the proposed method, compared with the traditional polarimetric classification.

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Damage assessment in urban areas using post-earthquake airborne PolSAR imagery

Cited by 48 publications

References 31 publications

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

Woodland Extraction from High-Resolution CASMSAR Data Based on Dempster-Shafer Evidence Theory Fusion

A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure

Fast building damage mapping using a single post-earthquake PolSAR image: a case study of the 2010 Yushu earthquake

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