Papers › Deep Learning for Landslide Recognition in Satellite Architecture
Deep Learning for Landslide Recognition in Satellite Architecture
Trong-An Bui, Pei-Jun Lee, Kai-Yew Lum, Clarissa Loh, Kyo Tan
Using the optical camera in remote sensing is limited in various environmental conditions. This paper presents a system of combining deep learning and image transform algorithms to detect landslide location in satellite images. In the deep learning part, a convolution neural network is used to classify satellite images contain landslides. From landslide images classified, in order to accurately identify landslides under different lighting conditions, this paper proposes a transformation algorithm Hue - Bi-dimensional empirical mode decomposition (H-BEMD) to determine the landslide region and size. After the location of landslide is detected, we discover the size change of the landslide based on different time points. In this study, we record an accuracy of up to 96% in the classification process, and the accuracy of landslide location almost absolute.
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Introduced by this paper: H-BEMD
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