Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery
This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable re...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/22/4698 |
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| author | Hejar Shahabi Maryam Rahimzad Sepideh Tavakkoli Piralilou Omid Ghorbanzadeh Saied Homayouni Thomas Blaschke Samsung Lim Pedram Ghamisi |
| author_facet | Hejar Shahabi Maryam Rahimzad Sepideh Tavakkoli Piralilou Omid Ghorbanzadeh Saied Homayouni Thomas Blaschke Samsung Lim Pedram Ghamisi |
| author_sort | Hejar Shahabi |
| collection | DOAJ |
| description | This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection. |
| first_indexed | 2024-03-10T05:05:20Z |
| format | Article |
| id | doaj.art-c1812dc727b74ae59124462201e82446 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:05:20Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c1812dc727b74ae59124462201e824462023-11-23T01:21:50ZengMDPI AGRemote Sensing2072-42922021-11-011322469810.3390/rs13224698Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 ImageryHejar Shahabi0Maryam Rahimzad1Sepideh Tavakkoli Piralilou2Omid Ghorbanzadeh3Saied Homayouni4Thomas Blaschke5Samsung Lim6Pedram Ghamisi7Center Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC G1K 9A9, CanadaCenter Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC G1K 9A9, CanadaDepartment of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, AustriaInstitute of Advanced Research in Artificial Intelligence (IARAI), Landstraßer Hauptstraße 5, 1030 Vienna, AustriaCenter Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, QC G1K 9A9, CanadaDepartment of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, AustriaSchool of Civil and Environmental Engineering, the University of New South Wales, Sydney, NSW 2032, AustraliaInstitute of Advanced Research in Artificial Intelligence (IARAI), Landstraßer Hauptstraße 5, 1030 Vienna, AustriaThis paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled dataset for their training step. As an alternative, in this paper, we developed an unsupervised learning model by employing a convolutional auto-encoder (CAE) to deal with the problem of limited labeled data for training. The CAE was used to learn and extract the abstract and high-level features without using training data. To assess the performance of the proposed approach, we used Sentinel-2 imagery and a digital elevation model (DEM) to map landslides in three different case studies in India, China, and Taiwan. Using minimum noise fraction (MNF) transformation, we reduced the multispectral dimension to three features containing more than 80% of scene information. Next, these features were stacked with slope data and NDVI as inputs to the CAE model. The Huber reconstruction loss was used to evaluate the inputs. We achieved reconstruction losses ranging from 0.10 to 0.147 for the MNF features, slope, and NDVI stack for all three study areas. The mini-batch K-means clustering method was used to cluster the features into two to five classes. To evaluate the impact of deep features on landslide detection, we first clustered a stack of MNF features, slope, and NDVI, then the same ones plus with the deep features. For all cases, clustering based on deep features provided the highest precision, recall, F1-score, and mean intersection over the union in landslide detection.https://www.mdpi.com/2072-4292/13/22/4698landslide mappingremote sensingunsupervised feature learningconvolutional auto-encoder (CAE)mini-batch K-means |
| spellingShingle | Hejar Shahabi Maryam Rahimzad Sepideh Tavakkoli Piralilou Omid Ghorbanzadeh Saied Homayouni Thomas Blaschke Samsung Lim Pedram Ghamisi Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery Remote Sensing landslide mapping remote sensing unsupervised feature learning convolutional auto-encoder (CAE) mini-batch K-means |
| title | Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery |
| title_full | Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery |
| title_fullStr | Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery |
| title_full_unstemmed | Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery |
| title_short | Unsupervised Deep Learning for Landslide Detection from Multispectral Sentinel-2 Imagery |
| title_sort | unsupervised deep learning for landslide detection from multispectral sentinel 2 imagery |
| topic | landslide mapping remote sensing unsupervised feature learning convolutional auto-encoder (CAE) mini-batch K-means |
| url | https://www.mdpi.com/2072-4292/13/22/4698 |
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