Spectral and spatial classification of earthquake images by support vector selection and adaptation

Kaya, Gulsen Taskm; Ersoy, Okan K.; Kamasak, Mustafa E.

doi:10.1109/socpar.2010.5686099

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Feathers of the intact building and collapsed building can be extracted respectively via mathematical morphology with different shaped structuring elements. Over recent years, several morphological methods have been proposed for building detection and a series of results have been obtained [32][33][34][35][36][37][38], Zhang proposed a straightforward building detection method based on the observation that vegetation can be filtered on significantly smaller filtering scales [39]. Vu considered LiDAR data within a multi-scale morphological space to conduct building detection via some clustering method [40].…”

Section: Introductionmentioning

confidence: 99%

Rapid Identification of Post-Earthquake Collapsed Buildings via Multi-Scale Morphological Profiles With Multi-Structuring Elements

et al. 2020

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Rapid identification of post-earthquake collapsed buildings can be conducive to capturing an immediate disaster assessment, which can help design effective emergency response strategies. At present, earthquake disaster assessment in practice mainly relies on time-consuming artificial field investigations, which cannot adapt to the demands of a timely rescue. However, morphological methods have proved to be of great potential in describing the disaster characteristics of building as for size, shape, directionality and contrast. Conventionally, boundaries of intact building appear flat and smooth, thus it can be reasonably depicted by linear structural element, while collapsed buildings generally emerge dispersedly distributed without regular geometry which can be suitably described by disk structural element. Based on these intuitive findings, we propose an approach for extracting intact and collapsed buildings via multi-scale morphological profiles with multi-structuring elements from post-earthquake satellite imagery. This approach consists of three core components: 1) Linear and disk structuring elements are established through top-hat reconstruction for extracting the initial intact and collapsed building, respectively. 2) Purified intact and collapsed buildings can be obtained by a straightforward threshold segmentation and further post processing, such as area, normalized green plant index (NGPI) and the length-width ratio for image noise suppression. 3) Boolean set operations are adopted to distinguish the intact and collapsed buildings. Experiments demonstrate that the proposed method has achieved satisfactory results and exerted great superiority in computational efficiency. INDEX TERMS Morphological profile, Earthquake damage, Building detection, Differential morphological profiles.

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Section: Introductionmentioning

confidence: 99%

Rapid Identification of Post-Earthquake Collapsed Buildings via Multi-Scale Morphological Profiles With Multi-Structuring Elements

et al. 2020

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“…Similarly, spatial filters (such as Canny filters and Gabor filters) were also proposed for the extraction of spatial features in the context of postearthquake VHR images. 2,16,17 However, these techniques rely on a prior fixed, albeit usually rich choice of a suitable data representation, which depends on the knowledge of the analyst and on the specificities of the image at hand. As a result, a few image classification methods in these studies can be practically used under time pressure.…”

Section: Introductionmentioning

confidence: 99%

Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images

Huang

Sun

Zhang

et al. 2019

J. Appl. Rem. Sens.

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Classifying land use from postearthquake very high-resolution (VHR) images is challenging due to the complexity of objects in Earth surface after an earthquake. Convolutional neural network (CNN) exhibits satisfied performance in differentiating complex postearthquake objects, thanks to its automatic extraction of high-level features and accurate identification of target geo-objects. Nevertheless, in view of the scale variance of natural objects, the fact that CNN suffers from the fixed receptive field, the reduced feature resolution, and the insufficient training sample has severely contributed to its limitation in the rapid damage mapping. Multiscale segmentation technique is considered as a promising solution as it can generate the homogenous regions and provide the boundary information. Therefore, we propose a combined multiscale segmentation convolutional neural network (CMSCNN) method for postearthquake VHR image classification. First, multiscale training samples are selected based on segments derived from the multiscale segmentation. Then, CNN is directly trained to classify the original image to further produce the preliminary classification maps. To enhance the localization accuracy, the output of CNN is further refined using multiscale segmentations from fine to coarse iteratively to obtain the multiscale classification maps. As a result, the combination strategy is able to capture objects and image context simultaneously. Experimental results show that the proposed CMSCNN method can reflect the multiscale information of complex scenes and obtain satisfied classification results for mapping postearthquake damage using VHR remote sensing images.

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“…The recognition process of those damage patterns can be performed by analysing features extracted either at pixel or region level (Dong and Shan, 2013;Kaya et al, 2010;Miura et al, 2013). However, the pixel level analysis is not meaningful for very high spatial resolution images, particularly in the context of damage assessment, as the evidences are identified based on the characteristics of their radiometric distribution pattern, which can be captured more precisely at a region-or object-level.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Towards automated satellite image segmentation and classification for assessing disaster damage using data-specific features with incremental learning

Vetrivel

Kerle

Gerke

et al. 2016

GEOBIA 2016: Solutions and Synergies

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ABSTRACT:Automated damage assessment based on satellite imagery is crucial for initiating fast response actions. Several methods based on supervised learning approaches have been reported as effective for automated mapping of damages using remote sensing images. However, adopting these methods for practical use is still challenging, as they typically demand large amounts of training samples to build a supervised classifier, which are usually not readily available. With the advancement in technologies local and detailed damage assessment for individual buildings is being made available, for example through analysis of images captured by unmanned aerial vehicles, monitoring systems installed in buildings, and through crowdsourcing. Often such assessments are being done in parallel, with results becoming available progressively. In this paper, an online classification strategy is adopted where a classifier is built incrementally using the streaming damage labels from various sources as training samples, i.e. without retraining it from the scratch when new samples stream in. The Passive-Aggressive online classifier is used for the classification process. Apart from the classifier, the choice of image features plays a crucial role in the performance of the classification. The features extracted using recently reported deep learning approaches such as Convolutional Neural Networks (CNN), which learns features directly from images, have been reported to be more effective than conventional handcrafted features such as gray level co-occurrence matrix and Gabor wavelets. Thus in this study, the potential of CNN features is explored for online classification of satellite image to detect structural damage, and is compared against handcrafted features. The feature extraction and classification process is carried out at an object level, where the objects are obtained by over-segmentation of the satellite image. The proposed online framework for damage classification achieves a maximum overall accuracy of about 73%, which is comparable to that of batch classifier accuracy (74%) obtained for the same training and testing samples, however at a significantly lesser time and memory requirements. Moreover, the CNN features always significantly outperform handcrafted features.

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Spectral and spatial classification of earthquake images by support vector selection and adaptation

Cited by 6 publications

References 10 publications

Rapid Identification of Post-Earthquake Collapsed Buildings via Multi-Scale Morphological Profiles With Multi-Structuring Elements

Rapid Identification of Post-Earthquake Collapsed Buildings via Multi-Scale Morphological Profiles With Multi-Structuring Elements

Combined multiscale segmentation convolutional neural network for rapid damage mapping from postearthquake very high-resolution images

Towards automated satellite image segmentation and classification for assessing disaster damage using data-specific features with incremental learning

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