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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Veröffentlicht in:	IEEE geoscience and remote sensing letters Jg. 16; H. 6; S. 982 - 986
Hauptverfasser:	Lei, Tao, Zhang, Yuxiao, Lv, Zhiyong, Li, Shuying, Liu, Shigang, Nandi, Asoke K.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.06.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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>.
AbstractList	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>. 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, [Formula Omitted], [Formula Omitted], [Formula Omitted], [Formula Omitted]-[Formula Omitted], and [Formula Omitted].
Author	Lei, Tao Liu, Shigang Li, Shuying Nandi, Asoke K. Lv, Zhiyong Zhang, Yuxiao
Author_xml	– sequence: 1 givenname: Tao orcidid: 0000-0002-2104-9298 surname: Lei fullname: Lei, Tao email: leitao@sust.edu.cn organization: School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 2 givenname: Yuxiao surname: Zhang fullname: Zhang, Yuxiao email: xyu@sust.edu.cn organization: School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi'an, China – sequence: 3 givenname: Zhiyong orcidid: 0000-0003-2595-4794 surname: Lv fullname: Lv, Zhiyong email: Lvzhiyong_fly@hotmail.com organization: School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China – sequence: 4 givenname: Shuying orcidid: 0000-0003-3994-2874 surname: Li fullname: Li, Shuying email: angle_lisy@163.com organization: School of Automation, Xi'an University of Posts and Telecommunications, Xi'an, China – sequence: 5 givenname: Shigang surname: Liu fullname: Liu, Shigang email: shgliu@gmail.com organization: School of Computer Science, Shaanxi Normal University, Xi'an, China – sequence: 6 givenname: Asoke K. orcidid: 0000-0001-6248-2875 surname: Nandi fullname: Nandi, Asoke K. email: asoke.nandi@brunel.ac.uk organization: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, U.K
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Snippet	Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However,...
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SubjectTerms	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
Title	Landslide Inventory Mapping From Bitemporal Images Using Deep Convolutional Neural Networks
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