RapidAI4EO: Mono- and Multi-temporal Deep Learning models for Updating the CORINE Land Cover Product
| Autor: | Bhugra, Priyash, Bischke, Benjamin, Werner, Christoph, Syrnicki, Robert, Packbier, Carolin, Helber, Patrick, Senaras, Caglar, Rana, Akhil Singh, Davis, Tim, De Keersmaecker, Wanda, Zanaga, Daniele, Wania, Annett, Van De Kerchove, Ruben, Marchisio, Giovanni |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/IGARSS46834.2022.9883198 |
| Popis: | In the remote sensing community, Land Use Land Cover (LULC) classification with satellite imagery is a main focus of current research activities. Accurate and appropriate LULC classification, however, continues to be a challenging task. In this paper, we evaluate the performance of multi-temporal (monthly time series) compared to mono-temporal (single time step) satellite images for multi-label classification using supervised learning on the RapidAI4EO dataset. As a first step, we trained our CNN model on images at a single time step for multi-label classification, i.e. mono-temporal. We incorporated time-series images using a LSTM model to assess whether or not multi-temporal signals from satellites improves CLC classification. The results demonstrate an improvement of approximately 0.89% in classifying satellite imagery on 15 classes using a multi-temporal approach on monthly time series images compared to the mono-temporal approach. Using features from multi-temporal or mono-temporal images, this work is a step towards an efficient change detection and land monitoring approach. Comment: Published in IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium |
| Databáze: | arXiv |
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