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 |
Conference
Conference | 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 |
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Country/Territory | Japan |
City | Yokohama |
Period | 28/07/19 → 2/08/19 |
Keywords
- Coherence
- GLCM
- Land-cover mapping
- LSTM
- Morphological profile
- Recurrent neural networks
- Sentinel- 1
- Synthetic aperture radar (SAR)
- Time series