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Erschienen in:
2017 | OriginalPaper | Buchkapitel
An Altitude Based Landslide and Debris Flow Detection Method for a Single Mountain Remote Sensing Image
verfasst von : Tingting Sheng, Qiang Chen
Erschienen in: Image and Graphics
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Abstract
The altitude information of single remote sensing image may aid in detecting the natural disaster, such as landslide or debris flow. Accordingly, in this paper, an approach based on altitude is proposed to detect landslide and debris flow for a single mountain remote sensing image. Firstly, we extract the features of landslide and debris flow areas and introduce slow feature analysis (SFA) to improve the feature distinguishability. Then, machine learning and a training model are used to detect suspected landslide and debris flow areas. By using the altitude information calculated by dark channel prior, we analyze the altitude distribution of suspected areas to judge whether landslide and debris flow occur in these regions. The experimental results of multiple mountain remote sensing images with landslide or debris flow demonstrate that the proposed algorithm can accurately detect landslide debris flow areas in a single mountain remote sensing image.