Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf
1 PublicationRapid Mapping of Landslides from Sentinel-2 Data Using Unsupervised Deep Learning
Shahabi, H.; Rahimzad, M.; Ghorbanzadeh, O.; Piralilou, S. T.; Blaschke, T.; Homayouni, S.; Ghamisi, P.
Abstract
This study investigates a pixel-based image analysis methodology built on unsupervised Deep Learning (DL) for rapid landslide detection. The utilized data includes the Minimum Noise Fraction (MNF) and Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 images and the topographic slope factor derived from the ALOS PALSAR sensor. We used a Convolutional auto-encoder (CAE) for extracting deep features from our input data. The Mini Batch K-means is then used for clustering the resulting deep features. The resulting landslide detection maps were then compared with a landslide inventory dataset for accuracy assessment. The proposed approach achieved the highest values of 76%, 91%, 83%, and 70% in terms of precision, recall, f1-score, and mIOU, respectively. This is the first study investigating unsupervised DL for landslide detection using Sentinel-2 images to the best of our knowledge.
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Contribution to proceedings
2022 IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS), 07.03.2022, Istanbul, Turkey
IEEE Mediterranean and Middle-East Geoscience and Remote Sensing Symposium (M2GARSS)
DOI: 10.1109/M2GARSS52314.2022.9840273
Cited 5 times in Scopus
Permalink: https://www.hzdr.de/publications/Publ-35712