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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE geoscience and remote sensing letters Ročník 16; číslo 6; s. 982 - 986
Hlavní autori: Lei, Tao, Zhang, Yuxiao, Lv, Zhiyong, Li, Shuying, Liu, Shigang, Nandi, Asoke K.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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>.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2889307