Automatic depicting algorithm of earthquake collapsed buildings with airborne high resolution image

Ma, Jianwen; Qin, Sixian

doi:10.1109/igarss.2012.6351400

Cited by 18 publications

(10 citation statements)

References 5 publications

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“…The improvement of the image sensors coupled with the aerial platforms have not only increased the amount of detail present in aerial images but have also increased the complexity of the automation of damage detection procedures [34]. Due to the high-resolution of the aerial imagery, object-based image analysis (OBIA) has started to be used to map damage [35][36][37] since objects in the scene are composed of a higher number of pixels. Instead of using the pixels directly, these approaches worked on the object level of an image composed of a set of pixels.…”

Section: Image-based Damage Mappingmentioning

confidence: 99%

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

et al. 2018

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Remote sensing images have long been preferred to perform building damage assessments. The recently proposed methods to extract damaged regions from remote sensing imagery rely on convolutional neural networks (CNN). The common approach is to train a CNN independently considering each of the different resolution levels (satellite, aerial, and terrestrial) in a binary classification approach. In this regard, an ever-growing amount of multi-resolution imagery are being collected, but the current approaches use one single resolution as their input. The use of up/down-sampled images for training has been reported as beneficial for the image classification accuracy both in the computer vision and remote sensing domains. However, it is still unclear if such multi-resolution information can also be captured from images with different spatial resolutions such as imagery of the satellite and airborne (from both manned and unmanned platforms) resolutions. In this paper, three multi-resolution CNN feature fusion approaches are proposed and tested against two baseline (mono-resolution) methods to perform the image classification of building damages. Overall, the results show better accuracy and localization capabilities when fusing multi-resolution feature maps, specifically when these feature maps are merged and consider feature information from the intermediate layers of each of the resolution level networks. Nonetheless, these multi-resolution feature fusion approaches behaved differently considering each level of resolution. In the satellite and aerial (unmanned) cases, the improvements in the accuracy reached 2% while the accuracy improvements for the airborne (manned) case was marginal. The results were further confirmed by testing the approach for geographical transferability, in which the improvements between the baseline and multi-resolution experiments were overall maintained.

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Section: Image-based Damage Mappingmentioning

confidence: 99%

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

et al. 2018

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“…Therefore, apart from a feature representation strategy, the choice of features that best discriminate the damaged and non-damaged regions is also a key element. Numerous studies reported that textures are the most influential feature for damage pattern recognition, as the damaged regions tend to show uneven and peculiar texture patterns, in contrast to non-damaged regions [28][29][30]. Many damage classification studies used statistical textures such as grey level co-occurrence matrix (GLCM)-based features for the damage pattern recognition [10,31,32].…”

Section: Introductionmentioning

confidence: 99%

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

et al. 2016

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Automatic post-disaster mapping of building damage using remote sensing images is an important and time-critical element of disaster management. The characteristics of remote sensing images available immediately after the disaster are not certain, since they may vary in terms of capturing platform, sensor-view, image scale, and scene complexity. Therefore, a generalized method for damage detection that is impervious to the mentioned image characteristics is desirable. This study aims to develop a method to perform grid-level damage classification of remote sensing images by detecting the damage corresponding to debris, rubble piles, and heavy spalling within a defined grid, regardless of the aforementioned image characteristics. The Visual-Bag-of-Words (BoW) is one of the most widely used and proven frameworks for image classification in the field of computer vision. The framework adopts a kind of feature representation strategy that has been shown to be more efficient for image classification-regardless of the scale and clutter-than conventional global feature representations. In this study supervised models using various radiometric descriptors (histogram of gradient orientations (HoG) and Gabor wavelets) and classifiers (SVM, Random Forests, and Adaboost) were developed for damage classification based on both BoW and conventional global feature representations, and tested with four datasets. Those vary according to the aforementioned image characteristics. The BoW framework outperformed conventional global feature representation approaches in all scenarios (i.e., for all combinations of feature descriptors, classifiers, and datasets), and produced an average accuracy of approximately 90%. Particularly encouraging was an accuracy improvement by 14% (from 77% to 91%) produced by BoW over global representation for the most complex dataset, which was used to test the generalization capability.

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“…Nadir aerial imagery readily depicts totally collapsed buildings or damaged roofs (Ma and Qin 2012). However, nadir imagery is physically constrained by its capture geometry and cannot directly observe the façades.…”

Section: * Corresponding Authormentioning

confidence: 99%

Towards a More Efficient Detection of Earthquake Induced Façade Damages Using Oblique Uav Imagery

Duarte¹,

Nex²,

Kerle³

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission I, ICWG I/IIKEY WORDS: multi-view, damage detection, urban search and rescue, salient object detection, deep learning ABSTRACT:Urban search and rescue (USaR) teams require a fast and thorough building damage assessment, to focus their rescue efforts accordingly. Unmanned aerial vehicles (UAV) are able to capture relevant data in a short time frame and survey otherwise inaccessible areas after a disaster, and have thus been identified as useful when coupled with RGB cameras for façade damage detection. Existing literature focuses on the extraction of 3D and/or image features as cues for damage. However, little attention has been given to the efficiency of the proposed methods which hinders its use in an urban search and rescue context. The framework proposed in this paper aims at a more efficient façade damage detection using UAV multi-view imagery. This was achieved directing all damage classification computations only to the image regions containing the façades, hence discarding the irrelevant areas of the acquired images and consequently reducing the time needed for such task. To accomplish this, a three-step approach is proposed: i) building extraction from the sparse point cloud computed from the nadir images collected in an initial flight; ii) use of the latter as proxy for façade location in the oblique images captured in subsequent flights, and iii) selection of the façade image regions to be fed to a damage classification routine. The results show that the proposed framework successfully reduces the extracted façade image regions to be assessed for damage 6 fold, hence increasing the efficiency of subsequent damage detection routines. The framework was tested on a set of UAV multi-view images over a neighborhood of the city of L'Aquila, Italy, affected in 2009 by an earthquake.

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Automatic depicting algorithm of earthquake collapsed buildings with airborne high resolution image

Cited by 18 publications

References 5 publications

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

Towards a More Efficient Detection of Earthquake Induced Façade Damages Using Oblique Uav Imagery

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