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Combined implementation of CaSSIS and HiRISE data through pansharpening experiments

Presentation #118.05 in the session Mission-supporting Practices, Modeling, and Data (Poster + Lightning Talk)

Published onOct 23, 2023
Combined implementation of CaSSIS and HiRISE data through pansharpening experiments

Since 2018, data collected by the Color and Stereo Surface Imaging System (CaSSIS), the primary imaging system onboard the ESA’s ExoMars TGO [1], represents one of the most important resources available for studying the surface of Mars. CaSSIS sensors produce multispectral images of Mars in four bands, spanning the blue, visible, and near Infrared wavelength range, thus enabling radiometrically calibrated surface colour/spectral observations at ≈ 4.6 m/px ground resolution from a circular 400 km orbit [2]. The High Resolution Imaging Science Experiment (HiRISE) aboard the NASA MRO is the highest-resolution orbital camera ever sent to Mars, achieving ≈ 0.25 m/px near-panchromatic images from a 255-320 km orbit [3]. Although HiRISE provides colour capabilities similar to CaSSIS, these are limited to a narrow swath of 1.2 km, significantly less than the CaSSIS coverage of 9.4 km. Such restricted coverage implies a limited potential for HiRISE colours for surface characterisation studies. However, in many cases, especially for areas of high scientific interest, CaSSIS observations overlap with HiRISE images. In the present work, we explore the possibility of combining the strengths of the two cameras by testing methodologies and developing tools to produce datasets with both the full-colour information of CaSSIS and the high resolution from HiRISE using Pansharpening. Pansharpening represents a family of data fusion and radiometric transformation techniques that aim to merge lower-resolution multispectral data with a high-resolution panchromatic base image. Different techniques have been in continuous development for almost forty years since the inception of his theory [4]. Although different methods are well established in Earth observation, they still need to be explored in planetary science. This work also presents a preliminary performance evaluation of various techniques, evaluated through quantitative and qualitative assessment indices [5]. Furthermore, the analyses and tools developed will prepare for future experiments on BepiColombo SIMBIO-SYS data to study Mercury [6].

Acknowledgements The study has been supported by the Italian Space Agency (ASI-INAF agreement no. 525 2017-03-17).

References [1] Thomas, N., et al. 2017, Space Sci. Rev., 212(3–4), 1897–1944. [2] Tornabene, L.L., et al. 2017, Space Sci. Rev., 214(1), 18. [3] McEwen, A.S., et al. 2007, JGR: Planets, 112(E5). [4] Meng, X., et al. 2021, IEEE Geosci. Remote Sens. Mag., 9(1), 18–52. [5] Du, Q., et al. 2007, GRSL-IEEE, 4(4), 518–522. [6] Cremonese, G. et al. 2020, Space Sci. Rev., 216(5), 75.

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