Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1181-1187 |
| Seitenumfang | 7 |
| Fachzeitschrift | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Jahrgang | 43 |
| Ausgabenummer | B3-2022 |
| Publikationsstatus | Veröffentlicht - 31 Mai 2022 |
| Veranstaltung | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III - Nice, Frankreich Dauer: 6 Juni 2022 → 11 Juni 2022 |
Abstract
The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Sozialwissenschaften (insg.)
- Geografie, Planung und Entwicklung
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in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, Jahrgang 43, Nr. B3-2022, 31.05.2022, S. 1181-1187.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Application of SAR Time-Series and Deep Learning for Estimating Landslide Occurrence Time
AU - Wang, W.
AU - Motagh, M.
AU - Plank, S.
AU - Orynbaikyzy, A.
AU - Roessner, S.
N1 - Funding Information: This study was supported by the Initiative and Networking Fund of the Helmholtz Association in the framework of the Helmholtz Alliance ‘‘Multi-Satellite Imaging for Satellite-based Landslide Occurrence and Warning Service (MultiSat4SLOWS)”.
PY - 2022/5/31
Y1 - 2022/5/31
N2 - The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure.
AB - The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure.
KW - Anomaly Detection
KW - Deep Learning
KW - Landslide
KW - SAR
KW - Unsupervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85131918219&partnerID=8YFLogxK
U2 - 10.5194/isprs-archives-XLIII-B3-2022-1181-2022
DO - 10.5194/isprs-archives-XLIII-B3-2022-1181-2022
M3 - Conference article
AN - SCOPUS:85131918219
VL - 43
SP - 1181
EP - 1187
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SN - 1682-1750
IS - B3-2022
T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III
Y2 - 6 June 2022 through 11 June 2022
ER -