DUYỆT THEO

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Tìm kiếm theo: Tác giả Vetrivel, A.

Duyệt theo: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
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Kết quả tìm kiếm từ 1 đến 4 trong 4 kết quả
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  • Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach

    • Tác giả: Vetrivel, A.;  Người hướng dẫn: -;  Người tham gia: - (2016)

  • 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...
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  • Potential of multi-temporal oblique airborne imagery for structural damage assessment

    • Tác giả: Vetrivel, A.;  Người hướng dẫn: -;  Người tham gia: - (2016)

  • Quick post-disaster actions demand automated, rapid and detailed building damage assessment. Among the available technologies, post-event oblique airborne images have already shown their potential for this task. However, existing methods usually compensate the lack of pre-event information with aprioristic assumptions of building shapes and textures that can lead to uncertainties and misdetections. However, oblique images have been already captured over many cities of the world, and the exploitation of pre- and post-event data as inputs to damage assessment is readily feasible in urban areas. In this paper, we investigate the potential of multi-temporal oblique imagery for detailed da...
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  • Towards automated satellite image segmentation and classification for assessing disaster damage using data-specific features with incremental learning

    • Tác giả: Vetrivel, A.;  Người hướng dẫn: -;  Người tham gia: - (2016)

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

    • Tác giả: Vetrivel, A.;  Người hướng dẫn: -;  Người tham gia: - (2016)

  • 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 suc...