Predicting Landsat Reflectance with Deep Generative Fusion

• URL: http://arxiv.org/abs/2011.04762v1
• Date: Mon, 9 Nov 2020 21:06:04 GMT
• Title: Predicting Landsat Reflectance with Deep Generative Fusion
• Authors: Shahine Bouabid, Maxim Chernetskiy, Maxime Rischard and Jevgenij Gamper
• Abstract summary: Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution. This hinders their potential to assist vegetation monitoring or humanitarian actions. We probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution as no single sensor provides fine-grained acquisitions with frequent coverage. This hinders their potential to assist vegetation monitoring or humanitarian actions, which require detecting rapid and detailed terrestrial surface changes. In this work, we probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics. We introduce a dataset of co-registered Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat surface reflectance time series and demonstrate the ability of our generative model to blend coarse daily reflectance information into low-paced finer acquisitions. We benchmark our proposed model against state-of-the-art reflectance fusion algorithms.

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