Yu Tao
Gather.town id
FMM08
Poster Title
Single Image Super-Resolution Restoration of TGO CaSSIS Colour Images
Institution
University College London
Abstract (short summary)
The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4-5m/pixel spatial resolution. CaSSIS has higher spatial resolution, image quality, and with colour bands, compared to the Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) images at 6m/pixel. However, the spatial resolution of CaSSIS is limited compared to the details revealed by the MRO High Resolution Imaging Science Experiment (HiRISE) images typically at 25-50cm/pixel resolution. Nevertheless, CaSSIS has much better global coverage compared to HiRISE (4% since 2006) and can provide more repeat and stereo observations in the future.
Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted about the nature of the Martian surface and how it formed or changes over time.
In this work, we introduce a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN; Tao et al., 2021) for single-image super-resolution restoration of TGO CaSSIS images and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over different science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface.
Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.
This research is supported by the UKSA Aurora programme (2018-2021) under grant ST/S001891/1.
Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted about the nature of the Martian surface and how it formed or changes over time.
In this work, we introduce a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN; Tao et al., 2021) for single-image super-resolution restoration of TGO CaSSIS images and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over different science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface.
Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.
This research is supported by the UKSA Aurora programme (2018-2021) under grant ST/S001891/1.
Plain text (extended) Summary
The ExoMars Trace Gas Orbiter (TGO)’s Colour and Stereo Surface Imaging System (CaSSIS) provides multi-spectral optical imagery at 4-5m/pixel spatial resolution. CaSSIS has higher spatial resolution, image quality, and with colour bands, compared to the Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) images at 6m/pixel. However, the spatial resolution of CaSSIS is limited compared to the details revealed by the MRO High Resolution Imaging Science Experiment (HiRISE) images typically at 25-50cm/pixel resolution. Nevertheless, CaSSIS has much better global coverage compared to HiRISE (<4% since 2006) and can provide more repeat and stereo observations in the future.
Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted about the nature of the Martian surface and how it formed or changes over time.
In this work, we introduce a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over different science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface.
Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.
Improving the spatial resolution of CaSSIS images would allow greater amounts of scientific information to be extracted about the nature of the Martian surface and how it formed or changes over time.
In this work, we introduce a novel Multi-scale Adaptive weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) for single-image super-resolution restoration of TGO CaSSIS images and demonstrate how this provides an effective resolution enhancement factor of about 3 times. We demonstrate with qualitative and quantitative assessments of CaSSIS SRR results over the Mars2020 Perseverance rover’s landing site. We also show examples of similar SRR performance over different science test sites mainly selected for being covered by HiRISE at higher resolution for comparison, which include many features unique to the Martian surface.
Application of MARSGAN will allow high resolution colour imagery from CaSSIS to be obtained over extensive areas of Mars beyond what has been possible to obtain to date from HiRISE.
URL
yu.tao@ucl.ac.uk
Poster file