Abstract
To date, the potential of multitemporal interferometric SAR (InSAR) data in land-cover mapping has not been fully explored despite suitable time series increasingly acquired from SAR sensors. Here, we suggest to use an LSTM (Long Short Term Memory) based land-cover classifier to address this problem. Spatial context is preserved by using grey-level spatial dependencies and morphological profiles. Further, a 4-LSTM-based model was trained to capture the temporal dynamics of InSAR coherence. Altogether 39 Sentinel-1 interferometric coherence pairs acquired over Donana in Spain were used to evaluate the method performance. Achieved more than 90% overall accuracy indicates the strong potential of developed InSAR recurrent approach in improving differentiation between various land cover classes.
Original language | English |
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Title of host publication | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 473-476 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-5386-9154-0, 978-1-5386-9153-3 |
ISBN (Print) | 978-1-5386-9155-7 |
DOIs | |
Publication status | Published - 2019 |
MoE publication type | A4 Article in a conference publication |
Event | 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019 https://igarss2019.org |
Conference
Conference | 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 |
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Abbreviated title | IGARSS 2019 |
Country/Territory | Japan |
City | Yokohama |
Period | 28/07/19 → 2/08/19 |
Internet address |
Funding
This study was supported by the National Natural Science Foundation of China (Grant Nos. 61671246). InSAR coherence time series were provided in the framework of the ESA SEOM SInCohMap project.
Keywords
- Coherence
- GLCM
- Land-cover mapping
- LSTM
- Morphological profile
- Recurrent neural networks
- Sentinel- 1
- Synthetic aperture radar (SAR)
- Time series