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This paper introduces SenPa-MAE, a transformer architecture that encodes the sensor parameters of an observed multispectral signal into the image embeddings.
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Filipponi, F.: Exploitation of Sentinel-2 time series to map burned areas at the national level: A case study on the 2017 Italy wildfires. Remote Sensing 11
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Kenton, J.D.M.W.C., Toutanova, L.K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proc. NAACL-HLT. pp. 4171–4186 (2019)
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Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proc. ECCV. pp. 213–229 (2020)
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Segarra, J., Buchaillot, M.L., Araus, J.L., Kefauver, S.C.: Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy 10
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Tian, C., Xu, Y., Li, Z., Zuo, W., Fei, L., Liu, H.: Attention-guided CNN for image denoising. Neural Networks 124
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Cong, Y., Khanna, S., Meng, C., Liu, P., Rozi, E., He, Y., Burke, M., Lobell, D., Ermon, S.: SatMAE: Pre-training transformers for temporal and multi-spectral satellite imagery. In: Proc. NeurIPS. pp. 197–211 (2022)
2022
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Fuller, A., Millard, K., Green, J.R.: SatViT: Pretraining transformers for earth observation. IEEE Geoscience and Remote Sensing Letters 19
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He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proc. CVPR. pp. 16000–16009 (2022)
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Pahlevan, N., Smith, B., Alikas, K., Anstee, J., Barbosa, C., Binding, C., Bresciani, M., Cremella, B., Giardino, C., Gurlin, D., et al.: Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment 270
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2020
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Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proc. CVPR. pp. 12179–12188 (2021)
2021
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Amatya, P., Kirschbaum, D., Stanley, T.: Rainfall-induced landslide inventories for lower mekong based on planet imagery and a semi-automatic mapping method. Geoscience Data Journal 9
2022
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Bachmann, R., Mizrahi, D., Atanov, A., Zamir, A.: MultiMAE: Multi-modal multi-task masked autoencoders. In: Proc. ECCV. pp. 348–367 (2022)
2022
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Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., et al.: ESA WorldCover 10 m 2021 v200 (2022), https://doi.org/10.5281/zenodo.7254221
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Reed, C.J., Gupta, R., Li, S., Brockman, S., Funk, C., Clipp, B., Keutzer, K., Candido, S., Uyttendaele, M., Darrell, T.: Scale-mae: A scale-aware masked autoencoder for multiscale geospatial representation learning. In: Proc. ICCV. pp. 4088–4099 (2023)
2023
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Schmitt, M., Ahmadi, S.A., Xu, Y., Taşkin, G., Verma, U., Sica, F., Hänsch, R.: There are no data like more data: Datasets for deep learning in earth observation. IEEE Geoscience and Remote Sensing Magazine 11
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