TY - JOUR
T1 - Landslide Recognition by Deep Convolutional Neural Network and Change Detection
AU - Shi, Wenzhong
AU - Zhang, Min
AU - Ke, Hongfei
AU - Fang, Xin
AU - Zhan, Zhao
AU - Chen, Shanxiong
N1 - Funding Information:
Manuscript received September 15, 2019; revised April 29, 2020 and June 25, 2020; accepted August 7, 2020. Date of publication August 21, 2020; date of current version May 21, 2021. This work was supported in part by the Hong Kong Polytechnic University Projects through the Landslip Prevention and Mitigation Programme, 2017, under Agreement CE 49/2017 (GE), and in part by the Ministry of Science and Technology of the People’s Republic of China under Project 2017YFB0503604. (Corresponding author: Min Zhang.) Wenzhong Shi is with the Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong (e-mail: [email protected]).
Publisher Copyright:
© 1980-2012 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - It is a technological challenge to recognize landslides from remotely sensed (RS) images automatically and at high speeds, which is fundamentally important for preventing and controlling natural landslide hazards. Many methods have been developed, but there remains room for improvement for stable, higher accuracy, and high-speed landslide recognition for large areas with complex land cover. In this article, a novel integrated approach combining a deep convolutional neural network (CNN) and change detection is proposed for landslide recognition from RS images. Logically, it comprises the following four parts. First, a CNN for landslide recognition is built based on training data sets from RS images with historical landslides. Second, the object-oriented change detection CNN (CDCNN) with a fully connected conditional random field (CRF) is implemented based on the trained CNN. Third, the preliminary CDCNN is optimized by the proposed postprocessing methods. Finally, the results are further enhanced by a set of information extraction methods, including trail extraction, source point extraction, and attribute extraction. Furthermore, in the implementation of the proposed approach, image block processing and parallel processing strategies are adopted. As a result, the speed has been improved significantly, which is extremely important for RS images covering large areas. The effectiveness of the proposed approach has been examined using two landslide-prone sites, Lantau Island and Sharp Peak, Hong Kong, with a total area of more than 70 km2. Besides its high speed, the proposed approach has an accuracy exceeding 80%, and the experiments demonstrate its high practicability.
AB - It is a technological challenge to recognize landslides from remotely sensed (RS) images automatically and at high speeds, which is fundamentally important for preventing and controlling natural landslide hazards. Many methods have been developed, but there remains room for improvement for stable, higher accuracy, and high-speed landslide recognition for large areas with complex land cover. In this article, a novel integrated approach combining a deep convolutional neural network (CNN) and change detection is proposed for landslide recognition from RS images. Logically, it comprises the following four parts. First, a CNN for landslide recognition is built based on training data sets from RS images with historical landslides. Second, the object-oriented change detection CNN (CDCNN) with a fully connected conditional random field (CRF) is implemented based on the trained CNN. Third, the preliminary CDCNN is optimized by the proposed postprocessing methods. Finally, the results are further enhanced by a set of information extraction methods, including trail extraction, source point extraction, and attribute extraction. Furthermore, in the implementation of the proposed approach, image block processing and parallel processing strategies are adopted. As a result, the speed has been improved significantly, which is extremely important for RS images covering large areas. The effectiveness of the proposed approach has been examined using two landslide-prone sites, Lantau Island and Sharp Peak, Hong Kong, with a total area of more than 70 km2. Besides its high speed, the proposed approach has an accuracy exceeding 80%, and the experiments demonstrate its high practicability.
KW - Change detection
KW - convolutional neural network (CNN)
KW - landslide
KW - remotely sensed (RS) images
UR - http://www.scopus.com/inward/record.url?scp=85094876139&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2020.3015826
DO - 10.1109/TGRS.2020.3015826
M3 - Journal article
AN - SCOPUS:85094876139
SN - 0196-2892
VL - 59
SP - 4654
EP - 4672
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 6
M1 - 9173780
ER -