A Framework of Rapid Regional Tsunami Damage Recognition From Post-event TerraSAR-X Imagery Using Deep Neural Networks

Bai, Yanbing; Gao, Chang; Singh, Sameer; Koch, Magaly; Adriano, Bruno; Mas, Erick; Koshimura, Shunichi

doi:10.1109/lgrs.2017.2772349

Cited by 70 publications

(53 citation statements)

References 19 publications

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“…The results of Wieland et al [16] and Bai et al [22] were chosen for this purpose. Both research groups used the same TerraSAR-X images as in the present study.…”

Section: Discussion Of the Case Studymentioning

confidence: 99%

“…Consequently, more robust and versatile calibration methods are needed. To this end, machine learning techniques have been successfully applied [16][17][18][19][20][21][22]. Calibration using supervised machine learning is performed based on training samples, that is a set of features for which the corresponding damage states are known in advance.…”

Section: Introductionmentioning

confidence: 99%

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Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

et al. 2018

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Although supervised machine learning classification techniques have been successfully applied to detect collapsed buildings, there is still a major problem that few publications have addressed. The success of supervised machine learning strongly depends on the availability of training samples. Unfortunately, in the aftermath of a large-scale disaster, training samples become available only after several weeks or even months. However, following a disaster, information on the damage situation is one of the most important necessities for rapid search-and-rescue efforts and relief distribution. In this paper, a modification of the supervised machine learning classification technique called logistic regression is presented. Here, the training samples are replaced with probabilistic information, which is calculated from the spatial distribution of the hazard under consideration and one or more fragility functions. Such damage probabilities can be collected almost in real time for specific disasters such as earthquakes and/or tsunamis. We present the application of the proposed method to the 2011 Great East Japan Earthquake and Tsunami for collapsed building detection. The results show good agreement with a field survey performed by the Ministry of Land, Infrastructure, Transport and Tourism, with an overall accuracy of over 80%. Thus, the proposed method can significantly contribute to a rapid estimation of the number and locations of collapsed buildings.

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“…The results of Wieland et al [16] and Bai et al [22] were chosen for this purpose. Both research groups used the same TerraSAR-X images as in the present study.…”

Section: Discussion Of the Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Unsupervised Classification of Collapsed Buildings Using Satellite Imagery, Hazard Scenarios and Fragility Functions

et al. 2018

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“…To fuse the two scores, a linear combination is performed according to Equation (5). where NS 1 and NS 2 are the normalized scores of the original image and target image, respectively; and ω 1 and ω 2 are the corresponding weights with a summation of 1.…”

Section: Target Recognition Via Score-level Fusionmentioning

confidence: 99%

“…The interpretation of synthetic aperture radar (SAR) images has important meanings for both civilian and military applications [1][2][3][4][5]. SAR images are interpreted for sea ice monitoring and classification in [2].…”

Section: Introductionmentioning

confidence: 99%

“…Gao et al examined the change detection in SAR images based on deep learning [3]. Bai et al analyzed SAR images to map earthquake-induced damages [4] and recognized regional tsunami damages [5] using machine learning techniques. This study focused on the automatic target recognition (ATR) of SAR images [1], which aims to determine the target type of an SAR image with unknown label by matching the information in the input SAR image with that in the training samples.…”

Section: Introductionmentioning

confidence: 99%

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Target Recognition in SAR Images Based on Information-Decoupled Representation

Chang

You

2018

Remote Sensing

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This paper proposes an automatic target recognition (ATR) method for synthetic aperture radar (SAR) images based on information-decoupled representation. A typical SAR image of a ground target can be divided into three parts: target region, shadow and background. From the aspect of SAR target recognition, the target region and shadow contain discriminative information. However, they also include some confusing information because of the similarities of different targets. The background mainly contains redundant information, which has little contribution to the target recognition. Because the target segmentation may impair the discriminative information in the target region, the relatively simpler shadow segmentation is performed to separate the shadow region for information decoupling. Then, the information-decoupled representations are generated, i.e., the target image, shadow and original image. The background is retained in the target image, which represents the coupling of target backscattering and background. The original image and generated target image are classified using the sparse representation-based classification (SRC). Then, their classification results are combined by a score-level fusion for target recognition. The shadow image is not used because of its lower discriminability and possible segmentation errors. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under both standard operating condition (SOC) and various extended operating conditions (EOCs). The proposed method can correctly classify 10 classes of targets with the percentage of correct classification (PCC) of 94.88% under SOC. With the PCCs of 93.15% and 75.03% under configuration variance and 45 • depression angle, respectively, the superiority of the proposed is demonstrated in comparison with other methods. The robustness of the proposed method to both uniform and nonuniform shadow segmentation errors is validated with the PCCs over 93%. Moreover, with the maximum average precision of 0.9580, the proposed method is more effective than the reference methods on outlier rejection.

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