Images Of Mars Research Articles

The Visual Monitoring Camera (VMC) on Mars Express: A new science instrument made from an old webcam orbiting Mars

The Visual Monitoring Camera (VMC) is a small imaging instrument onboard Mars Express with a field of view of ∼40°x30°. The camera was initially intended to provide visual confirmation of the separation of the Beagle 2 lander and has similar technical specifications to a typical webcam of the 2000s. In 2007, a few years after the end of its original mission, VMC was turned on again to obtain full-disk images of Mars to be used for outreach purposes. As VMC obtained more images, the scientific potential of the camera became evident, and in 2018 the camera was given an upgraded status of a new scientific instrument, with science goals in the field of Martian atmosphere meteorology. The wide Field of View of the camera combined with the orbit of Mars Express enable the acquisition of full-disk images of the planet showing different local times, which for a long time has been rare among orbital missions around Mars. The small data volume of images also allows videos that show the atmospheric dynamics of dust and cloud systems to be obtained. This paper is intended to be the new reference paper for VMC as a scientific instrument, and thus provides an overview of the updated procedures to plan, command and execute science observations of the Martian atmosphere. These observations produce valuable science data that is calibrated and distributed to the community for scientific use.

An Orbital Comparison of a Late Mantling Unit on Aeolis Mons With Other Erosion‐Resistant Strata Explored by MSL in Gale Crater, Mars

AbstractPrevious investigations along Curiosity's traverse in Gale crater have explored the relationship between orbital and in situ observations. This work aims to better understand the geologic environment of units only observable from orbit and compare them to the properties of units examined by Curiosity. Here, we map an erosion‐resistant dark‐toned mantling unit that overlies the modern topography of Aeolis Mons (informally known as Mt. Sharp) and compare this unit to two other previously mapped dark‐toned resistant units, the marker band and the mound skirting unit (MSU), that have been inferred to represent different geologic environments (lacustrine and aeolian, respectively). Visible to short wave infrared spectra from the Compact Reconnaissance Imaging Spectrometer for Mars and visual images from the High Resolution Imaging Science Experiment and Context Cameras aboard the Mars Reconnaissance Orbiter are used for this comparison. Spectral data suggest a mafic composition with minor alteration, although the composition varies more with location around Mt. Sharp rather than between units. Morphologically, the mantling unit has strong similarities to the marker band based on their consistent low‐albedo, erosion‐resistance, and smooth appearance, contrasting with the highly variable surface texture of the MSU. We hypothesize that all three units had a similar sediment source but experienced aqueous alteration at different times: early ubiquitous cementation in a surface aqueous environment in the mantling unit and marker band versus patchy late diagenesis in the MSU. If true, these results suggest that water activity continued within the Gale crater long after the erosion of Mt. Sharp.

Light4Mars: A lightweight transformer model for semantic segmentation on unstructured environment like Mars

Auto-semantic segmentation is important for robots on unstructured and dynamic environments like planets where ambient conditions cannot be controlled and the scale is larger than that found indoors. Current learning-based methods have achieved breathtaking improvements on this topic. For onboard applications, however, all those methods still suffer from huge computational costs and are difficult to deploy on edge devices. In this paper, unlike previous transformer-based SOTA approaches that heavily relied on complex design, we proposed Light4Mars, a lightweight model with minimal computational complexity while maintaining high segmenting accuracy. We designed a lightweight squeeze window transformer module that focuses on window-scale feature extraction and is more effective in learning global and local contextual information. The aggregated local attention decoder is utilized to fuse semantic information at different scales, especially for unstructured scenes. Since there are few all-terrain datasets for semantic segmentation of unstructured scenes like Mars, we built a synthetic dataset SynMars-TW, referencing images collected by the ZhuRong rover on the Tianwen-1 mission and the Curiosity rover. Extensive experiments on SynMars-TW and the real Mars dataset, MarsScapes show that our approach achieves state-of-the-art performance with favorable computational simplicity. To the best of our knowledge, the proposed Light4Mars-T network is the first segmentation model for Mars image segmentation with parameters lower than 0.1M. Code and datasets are available at https://github.com/CVIR-Lab/Light4Mars.

Current Status of the Community Sensor Model Standard for the Generation of Planetary Digital Terrain Models

The creation of accurate elevation models (topography) from stereo images are critical for a large variety of geospatial activities, including the production of digital orthomosaics, change detection, landing site analysis, geologic mapping, rover traverse planning, and spectral analysis. The United Stated Geological Survey, Astrogeology Science Center, continues to transition the supported planetary sensor models to the Community Sensor Model (CSM) standard. This paper describes the current state of use for this photogrammetric standard, supported sensor model types, and qualitatively compares derived topography between SOCET SET and SOCET GXP (®BAE Systems) using HiRISE stereo images of Mars. Our transition to the CSM standard will ensure an uninterrupted capability to make these valuable products for Mars and many other extraterrestrial planets and moons.

Limits on polygonal organization of boulders in the Martian northern lowlands

Polygonal terrain on Mars can form via thermal contraction and subsequent fracturing of the permafrost layer and covers much of the surface poleward of ∼60°. In similar terrains on Earth, seasonal freeze-thaw processes create a range of diverse landforms, including several in which clasts on the surface congregate into sorted circles and polygons. In the Martian northern lowlands, several investigations into boulder patterns have come to differing conclusions on whether analogous organization of clasts is present on Mars, whether there is an association between boulders and polygonal fracture margins, and what periglacial process may cause such organization in the modern environment that does not support seasonal melt. To address this discrepancy, we identify and measure boulders in the Martian northern lowlands with the Martian Boulder Automatic Recognition System (MBARS) and assess boulder spatial patterns to determine if boulders are organized into the margins of underlying fracture polygons. Sixty (60) Images from the High-Resolution Imaging Science Experiment (HiRISE) camera aboard the Mars Reconnaissance Orbiter (MRO) with priorly identified and measured polygons make up our survey, in which MBARS characterized 20 million boulders. We find that boulder patterns are not random across the northern lowlands and tend to be clustered with varying intensity. However, analysis of boulder pairwise distances shows that boulders are not generally organized into the 5–10 m polygonal patterns expected from an alignment of boulders to fracture margins. The lack of widespread polygonal organization of boulders indicates that processes responsible for the modern fracture polygons cannot organize meter-scale boulders towards their margins. This greatly reduces the likelihood of any terrestrial-like freeze-thaw organization occurring since the formation of the modern polygonal terrain in the Martian northern lowlands. Isolated instances of boulder patterns consistent with polygonal organization are found at the northern end of our survey. These instances could indicate a restriction of boulder-organizing processes only to the near-polar terrains but are better explained as selective preservation of paleo-organization. Plain language summaryWe have abundant evidence that the near-polar terrains on Mars have water ice buried at or very near the surface, much like on Earth. As this ice-rich surface cools in the winter, regularly spaced, roughly hexagonal cracks appear forming so-called “polygonal terrain”. In similar permafrost-dominated terrains on Earth, the presence of liquid water near the surface can cause rocks on the surface to be pushed outward during freezing. This eventually leads to the formation of stone circles or stone piles outlining the polygons, often forming large networks of sorted polygons. Using high-resolution images of Mars, we can see boulders as small as 1 m across, and it has been suggested that these boulders might also be organized into the edges of polygons on Mars. This raised the question: did these patterns form through Earth-like, wet processes? Or is there a way to make these patterns without liquid water? To answer this question, we surveyed sixty (60) images of Mars and used the Martian Boulder Automatic Recognition System (MBARS) to identify and measure the boulders in each image, totaling 20 million boulders. We determined that the boulders are not organized into a polygonal pattern, except in a few rare cases. Because the boulders are not organized, it is unlikely that any Earth-like wet processes or an unknown dry process is pushing boulders towards the edges of the fracture polygons. In the few places we do see this organization, it might be a pattern formed sometime in Mars' past when liquid water could exist at or near the surface.

Atmosphere-aware photoclinometry for pixel-wise 3D topographic mapping of Mars

High-resolution topographic mapping is essential for scientific investigations and operational exploration of planets, such as Mars. Photoclinometry, which uses light scattered from a surface to reconstruct 3D topography, can retrieve subtle topographic details from monocular images. However, its performance is affected by the atmosphere of Mars, which alters surface reflectance mechanisms due to the scattering and absorption by aerosols. Therefore, specific treatments accounting for these atmospheric effects are needed to enable photoclinometric mapping of Mars. This paper presents a novel photoclinometric approach incorporating a radiative-transfer model of atmospheric scattering effects for pixel-wise 3D reconstruction of the Martian surface. The approach requires a high-resolution image, a corresponding coarse-resolution digital elevation model (DEM), and information of the optical depth as inputs. A radiative transfer model adapted to the Martian atmosphere is used to account for the atmospheric effects. The approach also allows directly adopting optical depth estimates from a global database (e.g., the Mars Climate Database). The approach was validated using different types of Mars orbiter images collected by cameras on-board the Mars Reconnaissance Orbiter and the Tianwen-1 Orbiter. The results indicate that the approach can achieve a geometric accuracy (in terms of root-mean-squared error (RMSE) of the elevation) of approximately 2 pixels of the image resolution and significantly enhances topographic details. In addition, we evaluated the approach using different settings for the optical depths and spatial resolutions of the input DEMs. The results show that overestimating the optical depth leads to overestimation of topographic amplitudes (e.g., deeper craters). On the other hand, underestimating the optical depth by the same amount as overestimation leads to a smaller increase in the RMSE. Moreover, coarsening the resolution of an input DEM increases the RMSE of the photoclinometric results. Nevertheless, photoclinometry improves both the RMSE and the resolution of the input DEM. This new approach serves as an effective means for applying photoclinometry for pixel-wise 3D topographic mapping of the Martian surface. This will facilitate exploitation of the large number of high-resolution monocular images of Mars in 3D topographic mapping of the planet.

Producing 3D radargrams from orbital radar sounding data at Mars: History, results, methods, lessons and plans

The Shallow Radar (SHARAD) instrument aboard Mars Reconnaissance Orbiter (MRO) has been conducting sounding operations of Mars' near surface for more than 16 years. Results of the SHARAD investigation have been well documented in the literature, with relatively high coverage density in the polar regions leading to some of the earliest scientific discoveries there. Data collection and ensuing surprises of SHARAD's primary and first extended science phases were the impetus for choosing the north polar region for producing a first 3D radar image. This early attempt was successful, producing the first 3D radar image of Mars' north polar layered deposits in Planum Boreum, and shortly thereafter a companion 3D radar image of the south polar layered deposits in Planum Australe. Subsequent work refined the 3D methodology used to produce these images, leading to a significantly higher quality 3D image of the former as well as the first 3D radar image of the debris-covered glaciers in the east-central portion of Deuteronilus Mensae (DM) in the mid-latitudes. Prior experience in terrestrial seismic data processing and analysis methods forms the basis of the 3D methodology used with Martian radar data, and has been indispensable in ongoing efforts to improve this methodology to further clarify the 3D images of the targets. The purposes of this article are to review 1) the SHARAD 3D work history and results, 2) the methodology developed and challenges encountered thus far in producing the SHARAD 3D images, 3) the broader impact on the processing and analysis of SHARAD data, and 4) current efforts and plans for producing follow-on SHARAD 3D images. Similarities and differences between orbital radar and seismic sounding and data processing are sprinkled throughout the article as reminders that the outcomes in this case are very much the product of cross-disciplinary knowledge and experience.

Hello World: Digital Media and the Search for Interplanetary Life on Religion’s Final Frontier

Abstract NASA’s 2021 Perseverance Rover races toward the farthest reaches of our known world to search for signs of life. Although promising to discover life on the surface of Mars, the rover’s digital images of Mars’s barren and scorched wasteland paradoxically reinforce the fear that death is inevitable. Why search for life on the barren wasteland of Mars? This article explores this question from the perspective of scholarly examinations of Spiritualism and argues that the search for extraterrestrial life on Mars’s surface constitutes a growing religious community, one that reframes religious practice around digital media’s interface and endows digital technologies with the potentiality to transform the burden of our impending extinction into an opportunity to extend humanity’s perceived temporal and spatial finitude.

Terrain classification using mars raw images based on deep learning algorithms with application to wheeled planetary rovers

Scene information plays a crucial role in motion control, attitude perception, and path planning for wheeled planetary rovers (WPRs). Terrain recognition is the fundamental component of scene recognition. Due to the rich information, visual sensors are usually used in terrain classification. However, teleoperation delay prevents WPRs from using visual information efficiently. End-to-end learning method of deep learning (DL) that does not need complex image preprocessing was proposed to deal with this issue. This paper first built a terrain dataset (consists of loose sand, bedrock, small rock, large rock, and outcrop) using real Mars images to directly support You Only Look Once (YOLOv5) to test its performance on terrain classification. Because the capability of end-to-end training scheme is positively correlated with dataset, the performance of YOLOv5 can be significantly improved by exploiting orders of magnitude more data. The best combination of hyperparameters and models was achieved by slightly tuning YOLOv5, and data augmentation was also applied to optimize its accuracy. Furthermore, its performance was compared with two other end-to-end network architectures. Deep learning algorithms can be used in the future planetary exploration missions, such as WPRs autonomy improvement, traversability analysis, and avoiding getting trapped.

Fully Controlled 6 Meters per Pixel Equatorial Mosaic of Mars From Mars Reconnaissance Orbiter Context Camera Images, Version 1

AbstractNASA's Mars Reconnaissance Orbiter (MRO) spacecraft has operated around Mars since March 2006. The Context Camera (CTX) aboard MRO has returned >125,000 images of Mars, mostly at 5–6 m per pixel (mpp), providing ≈99% coverage with good‐quality images. Reconstruction of MRO's orbit and camera pointing from ground tracking are offset from the Mars global coordinate system. This work focuses on correcting those data for the more than 50,000 images in the equatorial region of Mars, between ±30° latitude, which comprises 50% of Mars' surface area. Determining and making these corrections in a relative control (image‐to‐image) and absolute or full control (image‐to‐ground reference) greatly improves the utility of the image data. Based on this work, the 95th percentile of tie points were offset from the Mars coordinate system by ≈155 m, corresponding to ≈28 CTX pixels. Controlling the more than 50,000 images was accomplished through an efficient, automated approach with additional manual input and validation, described herein. The mosaic was generated with semi‐manual image order from these controlled data and is provided at both 6 mpp and 100 mpp. The mosaic is available to the community through NASA's Planetary Data System Imaging & Cartography Annex.

Semi-supervised Learning for Mars Imagery Classification and Segmentation

With the progress of Mars exploration, numerous Mars image data are being collected and need to be analyzed. However, due to the severe train-test gap and quality distortion of Martian data, the performance of existing computer vision models is unsatisfactory. In this article, we introduce a semi-supervised framework for machine vision on Mars and try to resolve two specific tasks: classification and segmentation. Contrastive learning is a powerful representation learning technique. However, there is too much information overlap between Martian data samples, leading to a contradiction between contrastive learning and Martian data. Our key idea is to reconcile this contradiction with the help of annotations and further take advantage of unlabeled data to improve performance. For classification, we propose to ignore inner-class pairs on labeled data as well as neglect negative pairs on unlabeled data, forming supervised inter-class contrastive learning and unsupervised similarity learning. For segmentation, we extend supervised inter-class contrastive learning into an element-wise mode and use online pseudo labels for supervision on unlabeled areas. Experimental results show that our learning strategies can improve the classification and segmentation models by a large margin and outperform state-of-the-art approaches.

MarsNet: Automated Rock Segmentation With Transformers for Tianwen-1 Mission

The Mars exploration mission of China named Tianwen-1 is being carried out as scheduled. The Navigation and Terrain Cameras (NaTeCam) equipped on the Zhurong Rover play an essential role in obstacle recognition. The main obstacles on the Martian surface are rocks of different sizes, which influence the path planning of Zhurong Rover in scientific exploration. Most existing semantic segmentation methods are based on the U-Net architecture with ResNet or other backbones, and features extracted by these methods lack of the long-range dependencies. To fully exploit the context information, we propose the MarsNet framework for Mars image, which combines Transformers with the convolutional neural network as the backbone, and hybrid dilated convolution (HDC) is also employed to the decoder path to help detect the huge rocks. Besides, since there are few open-source datasets for rock segmentation for Mars, we establish a segmentation dataset from Martian surface image, named TWMARS, captured by NaTeCam. Extensive experiments are conducted on TWMARS dataset, and the experimental results demonstrate that MarsNet achieves accurate rock segmentation and outperforms state-of-the-art methods. The source code is available at: https://github.com/BUPT-ANT-1007/MarsNet.

Highly Accurate Visual Method of Mars Terrain Classification for Rovers Based on Novel Image Features.

It is important for Mars exploration rovers to achieve autonomous and safe mobility over rough terrain. Terrain classification can help rovers to select a safe terrain to traverse and avoid sinking and/or damaging the vehicle. Mars terrains are often classified using visual methods. However, the accuracy of terrain classification has been less than 90% in read operations. A high-accuracy vision-based method for Mars terrain classification is presented in this paper. By analyzing Mars terrain characteristics, novel image features, including multiscale gray gradient-grade features, multiscale edges strength-grade features, multiscale frequency-domain mean amplitude features, multiscale spectrum symmetry features, and multiscale spectrum amplitude-moment features, are proposed that are specifically targeted for terrain classification. Three classifiers, K-nearest neighbor (KNN), support vector machine (SVM), and random forests (RF), are adopted to classify the terrain using the proposed features. The Mars image dataset MSLNet that was collected by the Mars Science Laboratory (MSL, Curiosity) rover is used to conduct terrain classification experiments. The resolution of Mars images in the dataset is 256 × 256. Experimental results indicate that the RF classifies Mars terrain at the highest level of accuracy of 94.66%.

An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of Mars

In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DTM) and thus obtaining a 3D spatial mapping, which is necessary for a better analysis of planetary surfaces. However, the entire reconstruction process performed with the stereo-matching algorithm can be time consuming and can generate many artifacts. Coupled with the lack of adequate stereo coverage, it can pose a significant obstacle to 3D planetary mapping. Recently, many deep learning architectures have been proposed for monocular depth estimation, which aspires to predict the third dimension given a single 2D image, with considerable advantages thanks to the simplification of the reconstruction problem, leading to a significant increase in interest in deep models for the generation of super-resolution images and DTM estimation. In this paper, we combine these last two concepts into a single end-to-end model and introduce a new generative adversarial network solution that estimates the DTM at 4× resolution from a single monocular image, called SRDiNet (super-resolution depth image network). Furthermore, we introduce a sub-network able to apply a refinement using interpolated input images to better enhance the fine details of the final product, and we demonstrate the effectiveness of its benefits through three different versions of the proposal: SRDiNet with GAN approach, SRDiNet without adversarial network, and SRDiNet without the refinement learned network plus GAN approach. The results of Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) are reported, applying the best model along all Oxia Planum tiles and releasing a 3D product enhanced by 4×.

Reduction of Metal Artifacts Caused by Titanium Peduncular Screws in the Spine by Means of Monoenergetic Images and the Metal Artifact Reduction Software in Dual-Energy Computed Tomography.

Objectives:To evaluate the reduction of metal artifacts in patients with titanium peduncular screws in the spine using (1) conventional images (CI), (2) virtual monoenergetic reconstructions (VMRs), and (3) VMR + Metal Artifact Reduction Software (VMR + MARS), with dual-energy computed tomography (DECT).Materials and Methods:Twenty-four patients with titanium peduncular screws in the spine were studied using a 64-channel DECT. During the postprocessing phase, the CI, the VMRs from 100 to 140 keV, and the VMR at 140 keV + MARS were synthesized. All the images were considered, and a quantitative evaluation was performed measuring the attenuation values (in terms of Hounsfield Units) with region of interest, in correspondence with the most hyperdense and hypodense artifacts. All the values were then compared. A qualitative evaluation, in terms of image quality and extent of artifacts, was also performed by two radiologists.Results:In quantitative terms, the 140 keV + MARS reconstruction was able to significantly reduce both bright and dark metal artifacts, compared to CI and to VMRs. The VMR was capable of significantly reducing both dark and bright artifacts, compared to CI. In qualitative terms, the VMR at 140 keV proved to be the best, compared to CI and VMR + MARS images.Conclusions:The VMR + MARS image reduces metal artifacts from titanium peduncular screws more than VMRs alone and CI. Furthermore, the VMR can decrease metal artifacts from a quantitative and a qualitative point of view. Combining information from VMRs and VMR + MARS images could be the best way to solve the issue of metal artifacts on computed tomography images.

Topographic Reconstruction of the “Tianwen-1” Landing Area on the Mars Using High Resolution Imaging Camera Images

High-resolution optical cameras have always been important scientific payloads in Mars exploration missions, and the Mars topographic data produced by their detection data can provide support for scientific research on Mars geomorphology and geological structure evolution. As of December 2021, there are still relatively few high-resolution image data at the sub-meter level on the Martian surface, with about 2.6% global coverage and even more limited stereo coverage (just about 0.4%). At the same time, there are still some difficulties in data acquisition and terrain reconstruction processing methods for high-resolution Mars images that need to be solved. This paper described how we designed the in-orbit stereo imaging strategy based on the characteristics of the High Resolution Imaging Camera (HiRIC) of China’s first Mars exploration mission (Tianwen-1), studied the technical solutions for HiRIC stereo image photogrammetry processing, and produced a topographic dataset for the “Tianwen-1” landing area, including digital orthophoto map (DOM) with a GSD (ground sample distance) of 0.7, 3.5m, and digital elevation model (DEM) with a GSD of 3.5m. Precision analysis results show that this topographic data has good consistency in planar position and elevation compared with the existing Mars terrain data, and has advantages in spatial resolution and terrain detail expression, which will be widely used in the geological background study of the “Tianwen-1” landing area, as well as landing site positioning, Martian surface remote operation planning and other Mars scientific research and engineering tasks.

DeepLabV3+/Efficientnet Hybrid Network-Based Scene Area Judgment for the Mars Unmanned Vehicle System.

Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system.

Clustering Supported Classification of ChemCam Data From Gale Crater, Mars

AbstractThe Chemistry and Camera (ChemCam) instrument on board the Mars Science Laboratory (MSL) rover Curiosity has collected a very large and unique data set of in‐situ spectra and images of Mars since landing in August 2012. More than 800,000 single shot laser‐induced breakdown spectroscopy (LIBS) spectra measured on more than 2,500 individual targets were returned so far by ChemCam. Such a data set is ideally suited for the application of statistical methods for the recognition of patterns that are difficult to observe by humans. In this work, we develop an approach relying on the feature extraction method Non‐Negative Matrix Factorization (NMF) and the repetition of k‐means clustering to classify ChemCam spectra. A strong consistency of the clustering results among the repetitions were found, which allowed us to identify six clusters representing the dominant compositions measured by ChemCam in Gale crater so far. By tracking clusters across the rover traverse from landing to sol 2756, we are able to provide a chemostratigraphic overview of the Gale crater from the ChemCam perspective. Transitions between major geologic groups (such as the Bradbury and the Mt. Sharp groups) are identifiable demonstrating that they are compositionally distinct, consistent with previous work. Compositional differences between their members also appear in the results. Furthermore, a first approach in which a random forest classifier was trained and validated with the obtained cluster assignments, reveals promising results for predicting cluster memberships of new ChemCam LIBS data acquired after sol 2756.

Recovering the Magnetic Image of Mars from Satellite Observations.

One of the possible approaches to reconstructing the map of the distribution of magnetization parameters in the crust of Mars from the data of the Mars MAVEN orbiter mission is considered. Possible ways of increasing the accuracy of reconstruction of the magnetic image of Mars are discussed.

Image Compression Software Design for Zhurong Mars Exploration Rover

Because of the limited number of information transmission channels and high bit error rate in Mars image transmission, a method of image compression software design and realization of Mars rover was proposed. The characteristics of rover image data management were analyzed, and the three capabilities that the rover image compression software needed to have were concluded: uniform file management capability, high-efficiency compression capability, and high fault tolerance capability. For uniform file management capability, a file system for uniform management of multiple types of image data was proposed. To meet the high efficiency and high fault tolerance requirements of the compression algorithm, based on run-length coding algorithm of first-1 bit, the three important processes of multi-load image unification processing, color image preprocessing, and segmented bit-plane image coding were expanded, to improve compression performance and fault tolerance. Then a complete set of image compression software design schemes was formed, using FPGA+DSP, and the complete set of software was realized. Finally a simulation verification system was used to verify the compression effect. Verification results show the effectiveness of the algorithm. This software has been verified by China’s Zhurong Mars rover and can effectively meet the needs of Mars exploration missions. The software design can be used as a reference for image compression in subsequent ultra-long-distance deep space explorations.