Loading...
Please wait, while we are loading the content...
Similar Documents
Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data
Content Provider | MDPI |
---|---|
Author | Michal, Podhorányi Milan, Lazecký Jan, Martinovič Svatoň, Václav Zitzlsberger, Georg |
Copyright Year | 2021 |
Abstract | Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least |
Starting Page | 3000 |
e-ISSN | 20724292 |
DOI | 10.3390/rs13153000 |
Journal | Remote Sensing |
Issue Number | 15 |
Volume Number | 13 |
Language | English |
Publisher | MDPI |
Publisher Date | 2021-07-30 |
Access Restriction | Open |
Subject Keyword | Remote Sensing Urban Change Detection Continuous Urban Monitoring Neural Network Sar Optical Multispectral Deep-temporal Ers-1 Ers-2 Landsat 5 Tm Sentinel 1 Sentinel 2 |
Content Type | Text |
Resource Type | Article |