Z. Yue

Global mapping and analysis of lunar wrinkle ridges

Lunar wrinkle ridges were globally mapped, and the morphological parameters, including length, width, elevation offset, and orientation, were also calculated. The results include the identification of 2839 segments with a total length of 25,560.69km, and the mean width, height, elevation offset, and orientation of all the wrinkle ridges are 3.70km, 0.31km, 46.37m, and -1.99 degrees, respectively, after weighted by the length. Based on their morphologies and distribution, lunar wrinkle ridges were classified into three categories: concentric, parallel, and isolated ridges, and most of the wrinkle ridges are distributed over basalts, although a few extend to nearby highlands. The relations between the morphological parameters were further quantitatively analyzed, and a similar linear correlation between the width and height was found in each class of lunar ridges, implying that small and large ridges were formed as a continuum and that the three classes of ridges were probably formed by some common processes. Finally, the relations between the lunar wrinkle ridges and other geomorphic phenomena were analyzed, indicating that purely volcanic origin or buried premare structures are difficult to reconcile with the investigation. In addition, the consistency between the occurrence of the lunar wrinkle ridges and the some thickness of lunar maria indicates that the formation of lunar wrinkle ridges is closely related to the lunar maria; however, the statistical NW or NE orientation of each class of lunar wrinkle ridges also suggests a suitable global stress field involved in their formation process.

Automated Rock Detection and Shape Analysis from Mars Rover Imagery and 3D Point Cloud Data

A new object-oriented method has been developed for the extraction of Mars rocks from Mars rover data. It is based on a combination of Mars rover imagery and 3D point cloud data. First, Navcam or Pancam images taken by the Mars rovers are segmented into homogeneous objects with a mean-shift algorithm. Then, the objects in the segmented images are classified into small rock candidates, rock shadows, and large objects. Rock shadows and large objects are considered as the regions within which large rocks may exist. In these regions, large rock candidates are extracted through ground-plane fitting with the 3D point cloud data. Small and large rock candidates are combined and postprocessed to obtain the final rock extraction results. The shape properties of the rocks (angularity, circularity, width, height, and width-height ratio) have been calculated for subsequent geological studies.

Mars Rover Localization based on Feature Matching between Ground and Orbital Imagery

Mars rover localization is usually realized with data from odometers, inertial measurement units, and stereo cameras. Location errors accumulate inevitably during any long-range rover traverse when data from only these ground-based sensors is employed. This paper presents a new approach to rover localization based on feature extraction and matching between ground (rover) and orbital imagery. This new approach can localize the rover in orbital imagery, eliminating the accumulated localization error and thereby improving the localization accuracy for long range rover traverse. The proposed approach is tested using NAVCAM images acquired by the Spirit and Opportunity rovers at multiple positions along with HIRISE orbital imagery covering the two landing sites. Results show that this new approach is effective in areas where there are outstanding rocks or outcroppings. The accuracy of this new localization approach is better than one pixel of the HIRISE image (which is 0.25 m).

Mars Surface Change Detection from Multi-temporal Orbital Images

A vast amount of Mars images have been acquired by orbital missions in recent years. With the increase of spatial resolution to metre and decimetre levels, fine-scale geological features can be identified, and surface change detection is possible because of multi-temporal images. This study briefly reviews detectable changes on the Mars surface, including new impact craters, gullies, dark slope streaks, dust devil tracks and ice caps. To facilitate fast and efficient change detection for subsequent scientific investigations, a featured-based change detection method is developed based on automatic image registration, surface feature extraction and difference information statistics. Experimental results which use multi-temporal images demonstrate the promising potential of the proposed method.