A high-performance multispectral adaptation GAN for harmonizing dense time series of Landsat-8 and Sentinel-2 images

Rocco Sedona, C. Paris, Gabriele Cavallaro, Lorenzo Bruzzone, Morris Riedel

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Abstract

The combination of data acquired by Landsat-8 and Sentinel-2 earth observation missions produces dense time series (TSs) of multispectral images that are essential for monitoring the dynamics of land-cover and land-use classes across the earth's surface with high temporal resolution. However, the optical sensors of the two missions have different spectral and spatial properties, thus they require a harmonization processing step before they can be exploited in remote sensing applications. In this work, we propose a workflow-based on a deep learning approach to harmonize these two products developed and deployed on an high-performance computing environment. In particular, we use a multispectral generative adversarial network with a U-Net generator and a PatchGan discriminator to integrate existing Landsat-8 TSs with data sensed by the Sentinel-2 mission. We show a qualitative and quantitative comparison with an existing physical method [National Aeronautics and Space Administration (NASA) Harmonized Landsat and Sentinel (HLS)] and analyze original and generated data in different experimental setups with the support of spectral distortion metrics. To demonstrate the effectiveness of the proposed approach, a crop type mapping task is addressed using the harmonized dense TS of images, which achieved an overall accuracy of 87.83% compared to 81.66% of the state-of-the-art method.
Original languageEnglish
Pages (from-to)10134 - 10146
Number of pages13
JournalIEEE Journal of selected topics in applied earth observations and remote sensing
Volume14
DOIs
Publication statusPublished - 27 Sep 2021
Externally publishedYes

Keywords

  • ITC-CV

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