Three-dimensional (3D) surface models, e.g., digital elevation models (DEMs), are important for planetary exploration missions and scientific research. Current DEMs of the Martian surface are mainly generated by laser altimetry or photogrammetry, which have respective limitations. Laser altimetry cannot produce high-resolution DEMs; photogrammetry requires stereo images, but high-resolution stereo images of Mars are rare. An alternative is the convolutional neural network (CNN) technique, which implicitly learns features by assigning corresponding inputs and outputs. In recent years, CNNs have exhibited promising performance in the 3D reconstruction of close-range scenes. In this paper, we present a CNN-based algorithm that is capable of generating DEMs from single images; the DEMs have the same resolutions as the input images. An existing low-resolution DEM is used to provide global information. Synthetic and real data, including context camera (CTX) images and DEMs from stereo High-Resolution Imaging Science Experiment (HiRISE) images, are used as training data. The performance of the proposed method is evaluated using single CTX images of representative landforms on Mars, and the generated DEMs are compared with those obtained from stereo HiRISE images. The experimental results show promising performance of the proposed method. The topographic details are well reconstructed, and the geometric accuracies achieve root-mean-square error (RMSE) values ranging from 2.1 m to 12.2 m (approximately 0.5 to 2 pixels in the image space). The experimental results show that the proposed CNN-based method has great potential for 3D surface reconstruction in planetary applications.
Rocks on the Martian surface exhibit signatures of geological activities. Rock distribution is also an important factor for landing site selection for exploration missions. This study investigated the rock abundance and erosion rate at the landing site of the Chinese Mars rover, Zhurong, in southern Utopia Planitia. A convolutional neural network based method was developed for rock detection from the images (0.7 m/pixel) obtained by using the high‐resolution imaging camera (HiRIC) onboard the Tianwen‐1 orbiter. Approximately 10,000 rocks with a minimum diameter of 1.4 m (2 pixels on the image) were extracted from the HiRIC images covering the Zhurong landing site and its neighboring region. According to the results, the rock abundance at the Zhurong landing site was approximately 5%. Measurements from the ground images acquired by the Zhurong rover after landing confirmed a rock abundance of approximately 5% at the landing site, consistent with the orbital measurement before landing. Furthermore, we investigated the rock erosion rate at the Zhurong landing region. Rock abundance was negatively correlated with the crater retention age and positively correlated with crater diameter. The obtained rock breakdown rate (0.054 nm/y to 0.074 nm/y) and surface denudation rate by filled volume of craters (0.104 nm/y to 0.209 nm/y) were in agreement with the values previously reported for the Amazonian period on Mars. The low erosion rate implies a dry climate and the absence of fluvial erosion processes in the Amazonian period.
Abstract. The paper presents our efforts on CNN-based 3D reconstruction of the Martian surface using monocular images. The Viking colorized global mosaic and Mar Express HRSC blended DEM are used as training data. An encoder-decoder network system is employed in the framework. The encoder section extracts features from the images, which includes convolution layers and reduction layers. The decoder section consists of deconvolution layers and is to integrate features and convert the images to desired DEMs. In addition, skip connection between encoder and decoder section is applied, which offers more low-level features for the decoder section to improve its performance. Monocular Context Camera (CTX) images are used to test and verify the performance of the proposed CNN-based approach. Experimental results show promising performances of the proposed approach. Features in images are well utilized, and topographical details in images are successfully recovered in the DEMs. In most cases, the geometric accuracies of the generated DEMs are comparable to those generated by the traditional technology of photogrammetry using stereo images. The preliminary results show that the proposed CNN-based approach has great potential for 3D reconstruction of the Martian surface.
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