Title: Recognition of the landslide disasters with extreme learning machine

Authors: Guanyu Chen; Xiang Li; Wenyin Gong; Hui Xu

Addresses: School of Computer Science, China University of Geosciences, Wuhan 430074, China ' School of Computer Science, China University of Geosciences, Wuhan 430074, China ' School of Computer Science, China University of Geosciences, Wuhan 430074, China ' School of Computer Science, China University of Geosciences, Wuhan 430074, China

Abstract: The geological disasters of landslides induced by the Wenchuan earthquake are great in number so landslide disaster recognition and investigation must be conducted in the early stage of large construction planning in the disaster area. In recent years, the studies on image recognition focus on the extreme learning machine algorithm. Based on the preprocessing of remote sensing images, this paper conducts landslide recognition with remote sensing images through the extreme learning machine classification combined with colour and texture features of ground objects. The comparison experiments of landslide recognition with the support vector machine algorithm shows that the recognition accuracy of the extreme learning machine algorithm is not much different from that of the SVM algorithm, but the extreme learning machine takes short time in training with absolute advantage.

Keywords: geological disaster; remote sensing image; extreme learning machine; landslide recognition; landslide disasters; image recognition; image classification; neural networks; computational science engineering.

DOI: 10.1504/IJCSE.2020.105215

International Journal of Computational Science and Engineering, 2020 Vol.21 No.1, pp.84 - 94

Received: 10 Jul 2017
Accepted: 29 Dec 2017

Published online: 11 Feb 2020 *

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