Year-end Sale % OFF 
Abstract
Sentinel-2 (S2) imagery is used in many research areas and for diverse applications. Its spectral resolution and quality are high but its spatial resolutions, of at most 10 m, is not sufficient for fine scale analysis. A novel method was thus proposed to super-resolve S2 imagery to 2.5 m. For a given S2 tile, the 10 S2 bands (four at 10 m and six at 20 m) were fused with additional images acquired at higher spatial resolution by the PlanetScope (PS) constellation. The radiometric inconsistencies between PS microsatellites were normalized. Radiometric normalization and super-resolution were achieved simultaneously using state-of–the-art super-resolution residual convolutional neural networks adapted to the particularities of S2 and PS imageries (including masks of clouds and shadows). The method is described in detail, from image selection and downloading to neural network architecture, training, and prediction. The quality was thoroughly assessed visually (photointerpretation) and quantitatively, confirming that the proposed method is highly spatially and spectrally accurate. The method is also robust and can be applied to S2 images acquired worldwide at any date.
Highlights
Satellite imagery provides a unique and detailed perspective on the state and changes in land, coastal, and oceanic ecosystems [1]
This study focused on image fusion from multiple sensors [4,13] with the goal to achieve super-resolution, i.e., further increasing the highest native spatial resolution [14]
We present an innovative method aiming at simultaneously normalizing PlanetScope radiometry and super-resolving Sentinel-2 imagery (10 bands from 10 or 20 to 2.5 m) using deep residual convolutional neural networks
Summary
Satellite imagery provides a unique and detailed perspective on the state and changes in land, coastal, and oceanic ecosystems [1]. The numerous studies focusing on fusion of remote sensing images have proposed various methods, each one adapted to the image characteristics and aiming at predefined objectives [4,5,7]. For multi-sensor image fusion, Shao et al [15] showed that previous methods, such as STARFM [16], ESTARFM [17], or ATPRK [18], were outperformed by CNNs. Sentinel-2 (S2) imagery (European Space Agency) is composed of 13 bands at different spatial resolutions: four bands at 10 m, six bands at 20 m, and three bands at 60 m. Its robustness was illustrated for six locations around the world with contrasted acquisition conditions
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
