Abstract
Bi-dimensional empirical mode decomposition (BEMD) transforms 2D data into some intrinsic mode functions (IMFs). In this paper, BEMD is used in image preprocessing to determine features of landslides. BEMD omits noisy areas of a landslide image, correctly identifying features of the landslide to conduct training through the Convolutional Neural Networks (CNN) architecture. In satellite image recognition technology, optimization of data transferring between the ground station and the satellite is important. Hence, using BEMD at the pre-training stage of CNN (identifying landslide identification area) reduces the size of the trained model up to 3 times compared to applying the normal CNN architecture with the original images.
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