Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China

Sun, Jing; Vu, Tuong Thuy

doi:10.1080/19475705.2016.1171257

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Then, in order to shorten the time to find the relative and global orientation, the Levenberg-Marquard algorithm was parallelized on multicore CPUs. In order to reduce the computation cost of object-based image analysis (OBIA) for quick earthquake damage assessment, a distributed OBIA approach was used in [114]. After splitting the images (including Quickbird, DEM, LiDAR, and thematic maps) of a large study area into small subimages, two or more computers could be used in parallel for analyzing the subimages.…”

Section: B Multisource Remote Sensing Data and Auxiliary Datamentioning

confidence: 99%

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Liu

Lei

et al. 2021

Proc. IEEE

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Despite the wide availability of remote sensing big data from numerous different Earth Observation (EO) instruments, the limitations in the spatial and temporal resolution of such EO sensors (as well as atmospheric opacity and other kinds of interferers) have led to many situations in which using only remote sensing data cannot fully meet the requirements of applications in which a (near) real-time response is needed. Examples of these applications include floods, earthquakes, and other kinds of natural disasters, such as typhoons. To address this issue, social media data have gradually been adopted to fill possible gaps in the analysis when remote sensing data are lacking or incomplete. In this case, the fusion of heterogeneous big data streams from multiple data sources introduces significant demands from a computational viewpoint. In order to meet these challenges,

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Section: B Multisource Remote Sensing Data and Auxiliary Datamentioning

confidence: 99%

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Liu

Lei

et al. 2021

Proc. IEEE

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Cumulative Ordinary Kriging interpolation model to forecast radioactive fallout, and its application to Chernobyl and Fukushima assessment: a new method and mini review

Külahçı

Şen

2022

Environ Sci Pollut Res

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Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach

Zhang

Liu

2020

Remote Sensing

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In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for “T1,” “T2,” and “T3,” respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. The results show that the best feature combination is different for the three datasets due to the variation of the feature separability.

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Distributed and hierarchical object-based image analysis for damage assessment: a case study of 2008 Wenchuan earthquake, China

Cited by 3 publications

References 26 publications

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

Cumulative Ordinary Kriging interpolation model to forecast radioactive fallout, and its application to Chernobyl and Fukushima assessment: a new method and mini review

Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach

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