Precision 3d Surface Reconstruction From Lro Nac Images Using Semi-Global Matching With Coupled Epipolar Rectification

Abstract

ABSTRACT:The Narrow-Angle Camera (NAC) on board the Lunar Reconnaissance Orbiter (LRO) comprises of a pair of closely attached highresolution push-broom sensors, in order to improve the swath coverage. However, the two image sensors do not share the same lenses and cannot be modelled geometrically using a single physical model. Thus, previous works on dense matching of stereo pairs of NAC images would generally create two to four stereo models, each with an irregular and overlapping region of varying size. Semi-Global Matching (SGM) is a well-known dense matching method and has been widely used for image-based 3D surface reconstruction. SGM is a global matching algorithm relying on global inference in a larger context rather than individual pixels to establish stable correspondences. The stereo configuration of LRO NAC images causes severe problem for image matching methods such as SGM, which emphasizes global matching strategy. Aiming at using SGM for image matching of LRO NAC stereo pairs for precision 3D surface reconstruction, this paper presents a coupled epipolar rectification methods for LRO NAC stereo images, which merges the image pair in the disparity space and in this way, only one stereo model will be estimated. For a stereo pair (four) of NAC images, the method starts with the boresight calibration by finding correspondence in the small overlapping stripe between each pair of NAC images and bundle adjustment of the stereo pair, in order to clean the vertical disparities. Then, the dominate direction of the images are estimated by project the center of the coverage area to the reference image and back-projected to the bounding box plane determined by the image orientation parameters iteratively. The dominate direction will determine an affine model, by which the pair of NAC images are warped onto the object space with a given ground resolution and in the meantime, a mask is produced indicating the owner of each pixel. SGM is then used to generate a disparity map for the stereo pair and each correspondence is transformed back to the owner and 3D points are derived through photogrammetric space intersection. Experimental results reveal that the proposed method is able to reduce gaps and inconsistencies caused by the inaccurate boresight offsets between the two NAC cameras and the irregular overlapping regions, and finally generate precise and consistent 3D surface models from the NAC stereo images automatically.

The Generation of High-Resolution Mapping Products for the Lunar South Pole Using Photogrammetry and Photoclinometry

“…Liu et al (2020) proposed an illumination-invariant matching algorithm to produce a localized DEM of the Chang'E-4 and Chang'E-5 landing areas [19]. Hu et al (2017) proposed a coupled epipolar rectification method for stereo LRO NAC images, and they introduced a semiglobal matching (SGM) algorithm for the matching of LRO NAC images and reduced the discrepancies in LRO NAC images [20]. Chen et al (2022) proposed a convolutional neural network (CNN)-based topography reconstruction method using LRO NAC images, which generated a DEM mosaic of the Chang'E-4 landing area in the range of 72.8 km × 30.3 km with a resolution of 1.5 m/pixel [21].…”