Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks

Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this let...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters Vol. 16; no. 6; pp. 982 - 986
Main Authors: Lei, Tao, Zhang, Yuxiao, Lv, Zhiyong, Li, Shuying, Liu, Shigang, Nandi, Asoke K.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Change detection
deep convolutional network
Feature extraction
Feature maps
Image color analysis
Image processing
Image reconstruction
Image segmentation
landslide inventory mapping (LIM)
Landslides
Mapping
multivariate morphological reconstruction (MMR)
Neural networks
Principal component analysis
Receptive field
Task analysis
Terrain factors
ISSN:1545-598X, 1558-0571
Online Access:Get full text
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Summary:Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, <inline-formula> <tex-math notation="LaTeX">Precision </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">Recall </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">Overall~Error </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">F </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">score </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">Accuracy </tex-math></inline-formula>.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2889307