Photogrammetric Processing of Tianwen-1 HiRIC Imagery for Precision Topographic Mapping on Mars

Abstract

The High-Resolution Imaging Camera (HiRIC) onboard China's Tianwen-1 Mars probe aims to acquire detailed imagery of the Martian surface to comprehensively investigate its topography and geomorphology. The HiRIC is a pushbroom camera comprising three CCDs to simultaneously achieve submeter resolution and a large swath. However, processing HiRIC images using the conventional photogrammetric workflow is difficult due to the large shifts and narrow overlapping among the CCD lines. This paper presents a novel approach for photogrammetric processing of HiRIC images for precision topographic mapping that incorporates 1) the fitting of the initial Rational Polynomial Coefficients (RPCs) of images from the HiRIC position and pointing data, 2) a deep-learning-based method for tie-point matching between adjacent CCD images and cross-orbit images, 3) the bundle adjustment of multiple CCD images for tripled-epipolar image generation to ensure inner-orbit consistency, 4) the block adjustment of multiple orbit images to ensure cross-orbit consistency, and 5) dense image matching and space intersection based on the refined RPCs to generate Digital Elevation Models (DEMs). Experimental analyses were conducted using HiRIC images covering the landing region of the Zhurong rover. The results revealed that subpixel accuracy was achieved for image residuals among multiple-CCD or multiple-orbit images. Comparison with the reference data (HiRISE and MOLA DEMs) revealed a mean deviation of less than 7 m in terms of the geometric accuracy and the subtle topographic details of the HiRIC DEM. The presented approach offers a reliable solution for using the new dataset of HiRIC imagery for Mars topographic mapping.

Large-Scale Block Bundle Adjustment of LROC NAC Images for Lunar South Pole Mapping Based on Topographic Constraint

“…The statistical values of re-projection residuals are usually used as the relative threshold values, which are determined based on the difference between the measured and calculated image coordinates. For example, Li et al [52] calculated the residual of each tie point during each iteration in the BBA of the HIRIC image in Tianwen-1 landing site. The residuals whose values exceed three times the mean square error (MSE) is considered outliers.…”

Fast Double-Channel Aggregated Feature Transform for Matching Planetary Remote Sensing Images

“…In recent years, with the rapid development of deep learning, more and more learning-based methods have been used to match PRSI [41][42][43][44]. Zhong et al [41] proposed a feature detection and description method for planetary images, which obtained a sparse and reliable set of feature points by learning the depth features of images, called Robust Planetary Features (RPFeat).…”

“…Li et al [42] use the convolutional neural network model (Net-model) to extract local features and global descriptors and use the nearest neighbor matching algorithm to match feature points. In addition, in order to accurately map the topography of Mars, Li et al [43] used Superpoint [45] to extract basic shape features (triangles, cubes, checkerboard, and stars), and used SuperGlue [46] to complete the matching of narrow overlapping regions between adjacent CCD images. Compared with traditional algorithms, the deep learning method has the advantages of automatically optimizing parameters and constructing required descriptors [14].…”

Rover Attitude and Camera Parameter: Rock Measurements on Mars Surface Based on Rover Attitude and Camera Parameter for Tianwen-1 Mission

“…Remote sensing data can be used to locate large rocks, the density of craters, and rock abundance [14,15], and it can be further utilized to evaluate the landing safety of a mission [16,17]. They are also regarded as a reference for studying the geological and topographical features of Mars [18]. Surface data are typically collected using scientific and engineering instruments on rovers [19], such as the Navigation and Terrain Camera (NaTeCam) [20] on Zhurong and Hazard Avoidance Camera (Hazcam) [21] and Supercam [22] on Perseverance.…”