Very-high-resolution rapid 3D mapping of Oxia Planum using cascaded MOLA-HRSC-CTX-HiRISE

 

Abstract

Stereo photogrammetry and/or photoclinometry have long been applied to the production of 3D imaging datasets for planetary surfaces. However, insufficient image resolution or reductions in the resultant 3D resolution, the lack of adequate stereo coverage and where available, lengthy processing time, various matching artefacts or noise effects, and unsatisfactory quality and complexity of processing, have long been significant barriers for large-area planetary 3D mapping. In this work, we demonstrate how deep learning based solutions can address the above issues using a single image and a coarse 3D reference as inputs.

1. Introduction

We demonstrate a very high-resolution rapid 3D mapping techniques and results over Oxia Planum, the forthcoming Rosalind Franklin ExoMars 2022 rover landing site. This uses a combination of deep learning based single-image super-resolution restoration (SRR), deep learning based single-image digital terrain model (DTM) estimation, and cascaded 3D co-alignment methods.

2. MARSGAN SRR

Improving the spatial resolution of Mars orbital images allows us to extract greater amounts of information about the nature of the surface, how it formed and has changed over time. One of the options to achieve a greater spatial resolution is through the use of SRR techniques. Recently, we have developed a single-image deep learning based SRR system, using Multi-scale Adaptive-weighted Residual Super-resolution Generative Adversarial Network (MARSGAN) [1], to be able to produce full-strip TGO CaSSIS SRR images within a few minutes. The MARSGAN SRR system can be similarly applied to MRO CTX and HiRISE images as demonstrated in [1].

3. MADNet DTM

The lack of adequate stereo coverage and where such coverage is available, the lengthy processing times, various artefacts and insufficient resolution, have long been big barriers for large-area planetary 3D mapping. Recently, we have developed a single-image deep learning based DTM extraction system, using Multi-scale generative Adversarial U-Net with Dense convolutional block and up-projection (MADNet) [2], to be able to produce high-quality, high-resolution, and artefact-free full-orbital strip CaSSIS DTM within a few seconds. The MADNet DTM system can be similarly applied to MEX HRSC, CTX, and HiRISE images as shown in [2]. Figure 1 shows examples of the MADNet DTM results compared to photogrammetric DTMs for HRSC (PSA MC11 product), CTX (SocetSet® product produced at NHM & OU), and HiRISE (SocetSet® PDS product).

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