Abstract
This article presents a novel dual-path full convolutional network (DP-FCN) model for constructing a landslide inventory map (LIM) with bitemporal very high-resolution (VHR) remote sensing images. Unlike traditional methods for drawing LIM, the proposed DP-FCN directly draws LIMs from the bitemporal aerial images with VHR through a trained deep neural network without generating the change magnitude map. Thus, the proposed approach can effectively reduce the effects of pseudo changes caused by phenological differences rather than landslide events. The proposed DP-FCN model contains two modules, namely, deep feature extraction, and joint feature learning networks. Deep feature extraction aims to reduce redundancy while extracting the high-level deep features from bitemporal images. Joint feature learning establishes the relationship between the deep features of bitemporal images and the ground reference map. Experiments on the real datasets of the landslide sites in Lantau Island of Hong Kong, China, demonstrate the feasibility and superiority of the proposed approach in drawing LIM with VHR remote sensing images. Moreover, compared with the results obtained by the state-of-the-art algorithms, the proposed DP-FCN method achieves the best performance in terms of accuracy for landslide inventory mapping.
Original language | English |
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Article number | 9036919 |
Pages (from-to) | 4575-4584 |
Number of pages | 10 |
Journal | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Volume | 13 |
DOIs | |
Publication status | Published - 2020 |
Bibliographical note
Funding Information:Manuscript received January 15, 2020; revised February 27, 2020; accepted March 11, 2020. Date of publication March 16, 2020; date of current version August 24, 2020. This work was supported in part by the National Key Research and Development Program of China under Grant 2017YFC0504501, in part by the National Natural Science Foundation of China under Grants 61501200, 61701396, and 61902313, and in part by the Natural Science Foundation of Shaan Xi Province under Grant 2017JQ4006. (Corresponding author: Xiang-Bing Kong.) ZhiYong Lv, TongFei Liu, and Cheng Shi are with the School of Computer Science and Engineering, Xi’An University of Technology, Xi’An 710048, China (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2008-2012 IEEE.
Other keywords
- Change detection (CD)
- landslide inventory map (LIM)
- natural disaster
- remote sensing images