RaVÆn: unsupervised change detection of extreme events with ML onboard satellites

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  • Bello, OM & Aina, YA Satellite Remote Sensing as a Tool for Disaster Management and Sustainable Development: Towards a Synergistic Approach. continued. society behavior science. 120365-373 (2014).

    article

    Google Scholar

  • Huyck, C., Verrucci, E. & Bevington, J. Remote sensing for disaster relief: A rapid, image-based perspective. in the Earthquake danger, risks and disasters 1-24 (Elsevier, 2014).

    Google Scholar

  • Mas, J.-F. Land Cover Change Monitoring: A Comparison of Change Detection Techniques. international J. Remote Sens. 20139-152 (1999).

    article

    Google Scholar

  • Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. & Lambin, E. Reviews Methods for detecting digital change in ecosystem monitoring: A review. international J. Remote Sens. 251565-1596 (2004).

    article

    Google Scholar

  • Jan, XX Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment (Wiley, 2021).

    A book

    Google Scholar

  • fritz, s. et al. A comparison of global agricultural surveillance systems and current gaps. agricultural. system 168258-272 (2019).

    article

    Google Scholar

  • Kothari, V., Liberis, E. & Lane, N.D. The final frontier: Deep learning in space. in the Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications45-49 (2020).

  • Selva, D. & Krejci, D. An overview and evaluation of CubeSats’ capabilities for Earth observation. Acta Astron. 7450-68 (2012).

    article

    Google Scholar

  • Boucheret, M.-L., Mortensen, I. & Favaro, H. Fast convolution filter banks for satellite payloads with on-board processing. IEEE J. Select. areas community. 17238-248 (1999).

    article

    Google Scholar

  • Velazco, R., Cheynet, P., Muller, J., Ecoffet, R. & Buchner, S. Robustness of artificial neural networks for onboard satellite imagery processing: Results of simulations and ground tests. IEEE Trans. nuclear Science. 442337-2344 (1997).

    To sue
    article

    Google Scholar

  • Griggin, M., Burke, H., Mandl, D. & Miller, J. Cloud cover detection algorithm for EO-1 Hyperion imagery. in the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003)Vol. 1, 86-89. https://doi.org/10.1109/IGARSS.2003.1293687 (2003).

  • Giuffrida, G. et al. CloudScout: A deep neural network for integrated cloud detection on hyperspectral imagery. remote sens. 122205 (2020).

    To sue
    article

    Google Scholar

  • Mateo Garcia, G. et al. Towards global flood mapping aboard low-cost satellites using machine learning. Science. representative 117249 (2021).

    CAS
    article

    Google Scholar

  • Hinz, R et al. Eo-Alert: Machine learning-based embedded satellite processing for very low latency convection storm nowcasting. in the EMCWF-ESA workshop (2020).

  • Kingma, DP & Welling, M. Auto-Encoding Variational Bayes (2013). arXiv:1312.6114.

  • Drusch, M. et al. Sentinel-2: ESA’s high-resolution optical mission for gmes operations services. Remote Sens. Environment. 12025-36 (2012).

    To sue
    article

    Google Scholar

  • Angerhausen, D., Bickel, VT & Adam, L. Unsupervised distributional learning for technosignature detection on the lunar surface. Earth Sp. Science. open arch. 201 (2020).

    Google Scholar

  • Merrill, N. & Eskandarian, A. Modified autoencoder training and scoring for robust unsupervised anomaly detection in deep learning. IEEE access (2020).

  • Reed, IS & Yu, X. Adaptive multiband Cfar detection of an optical pattern with unknown spectral distribution. IEEE Trans. acoustics. speech signal process. 381760-1770 (1990).

    To sue
    article

    Google Scholar

  • Caye Daudt, R., Le Saux, B. & Boulch, A. Fully folded Siamese networks for change detection. 2018 25th IEEE International Conference on Image Processing (ICIP) (2018).

  • Ruzicka, V., D’Aronco, S., Wegner, JD & Schindler, K. Deep active learning in remote sensing for data-efficient change detection. in the Proceedings of MACLEAN: Machine Learning for EARTH ObservatioN Workshop (ECML/PKDD 2020)Vol. 2766 (2020).

  • Çelik, T. Unsupervised Change Detection in Satellite Imagery Using Principal Component Analysis and (k)– means clustering. IEEE Geosci. Remote Sens. Lett. 2020 (2009).

    Google Scholar

  • Çelik, T. & Curtis, CV Resolution-selective detection of changes in satellite imagery. in the ICASSP (2010).

  • Cheng, Y., Li, H., Çelik, T. & Zhang, F. Frft-based improved algorithm for unsupervised change detection in SAR images via PCA and k-means clustering. in the IGARSS (2013).

  • Radke, RJ, Andra, S., Al-Kofahi, O. & Roysam, B. Image Change Detection Algorithms: A Systematic Survey. IEEE Trans. image process. 14294-307 (2005).

    To sue
    MathSciNet
    article

    Google Scholar

  • Gong, P. Change Detection Using Principal Component Analysis and Fuzzy Set Theory. Allowed to. J. Remote Sens. 1922-29 (1993).

    To sue
    article

    Google Scholar

  • de Jong, KL & Bosman, AS Unsupervised change detection in satellite imagery using convolutional neural networks. in the IJCNN (IEEE, 2019).

  • Kerner, HR et al. Generalized detection of changes on planetary surfaces using convolutional autoencoders and transfer learning. IEEE J. Select. Above. appl. Earth Observation Remote Sens. 123900-3918 (2019).

    To sue
    article

    Google Scholar

  • Giuffrida, G. et al. That (phi)-sat-1 mission: The first onboard demonstrator of deep neural networks for satellite earth observation. IEEE Trans. Geosci. remote sens. 601-14 (2021).

    article

    Google Scholar

  • Wagstaff, KL et al. Enable onboard detection of events of scientific interest for the Europa Clipper spacecraft. in the Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining2191-2201 (2019).

  • Cogliati, S. et al. The PRISMA imaging spectroscopy mission: overview and initial performance analysis. Remote Sens. Environment. 262112499. https://doi.org/10.1016/j.rse.2021.112499 (2021).

    To sue
    article

    Google Scholar

  • Copernicus Emergency Management System. Accessed 11/4/2021.

  • s2cloudless: Sentinel Hub’s cloud detector for Sentinel-2 imagery. Accessed 11/4/2021.

  • Brown, CF et al. Dynamic world, near real-time global 10m landuse landcover mapping. Science. Data 91-17 (2022).

    article

    Google Scholar

  • Saha, S. & Zhu, XX Patch-level unsupervised detection of planetary changes. IEEE Geosci. Remote Sens. Lett. 191-5 (2021).

    Google Scholar

  • Ballé, J., Laparra, V. & Simoncelli, EP End-to-End Optimized Image Compression. arXiv:1611.01704 (2016).

  • BLUE BOOK. Low-complexity lossless and near-lossless multispectral and hyperspectral image compression (2019).

  • Schelkens, P. et al. The JPEG 2000 family of standards. in the Wavelet Applications in Industrial Processing VI Vol. 7248 724802 (SPIE, 2009).

    Chapter

    Google Scholar

  • Ruzika, V. et al. Unsupervised change detection of extreme events with ML on-board. in the Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop, (NeurIPS 2021), Vancouver, Canada (2021). arXiv: 2111.02995.

  • Odena, A., Dumoulin, V. & Olah, C. Deconvolution and checkerboard artifacts. Distill 1e3 (2016).

    article

    Google Scholar

  • Wang, S.-Y., Wang, O., Zhang, R., Owens, A. & Efros, AA Cnn-generated images are surprisingly easy to recognize…for now. in the Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition8695-8704 (2020).

  • Rapuano, E. et al. An fpga-based hardware accelerator for cnns inference onboard satellites: benchmarking with myriad 2-based solutions for the Cloudscout case study. remote sens. 131518 (2021).

    To sue
    article

    Google Scholar

  • Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013).

  • Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. in the International Machine Learning Conference1597-1607 (PMLR, 2020).

  • McInnes, L., Healy, J. & Melville, J. Umap: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426 (2018).

  • Saha, S., Bovolo, F. & Bruzzone, L. Change detection in image time series using unsupervised LSTM. IEEE Geosci. Remote Sens. Lett. 2020 (2020).

    Google Scholar

  • Manas, O., Lacoste, A., Giró-i Nieto, X., Vazquez, D. & Rodriguez, P. Seasonal contrast: unsupervised pre-training from uncurated remote sensing data. in the Proceedings of the IEEE/CVF International Conference on Computer Vision9414-9423 (2021).

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