Spectral absorption features are important parameters for lunar mineral identification and its abundance retrieval. The spectral absorption features of lunar regolith around 1000 nm (Band I) and 2000 nm (Band II) are significant weakened due to long-history space weathering. Continuum removal can effectively enhance the features of spectral absorption for lunar surface materials, which is particularly applicable for processing the in-situ hyperspectral data that obtained by the Visible and Near Infrared Spectrometer (VNIS) onboard Chang’E-4 Yutu-2 Rover that landed in Von Kármán crater. In this paper, three types of functions, i.e., linear, the second-order parabola polynomial and the piecewise polynomial functions, are used to fit the continuum of VNIS spectra, and further accessing their influences on calculating the parameters of spectral absorption features. The study shows that the piecewise polynomial continuum performs the best in enhancing the spectral absorption features of VNIS data in Bands I and II. Both endpoints of the continuum can be found. The derived absorption features, such as band depth and centers are highly consistent with those obtained with ENVI software, who uses the full convex hull to determine spectral continuum. The difference between the piecewise polynomial method and full convex hull method in absorption band center and band area ratio is less than 1%. In addition, we tested the retrieval of Wo and Fs with the spectral feature parameters from different continuums and find that continuum composed by piecewise linear equation is appropriate.
KEYWORDS: Clouds, Space operations, LIDAR, Principal component analysis, Image processing, Image filtering, Data processing, Data conversion, Asteroids, Visualization
The OSIRIS-Rex Laser Altimeter (OLA) is the first scanning lidar instrument to fly a planetary mission. The OLA scans Bennu for about a month during the Orbit B mission phase and obtains 911 frames of point clouds. Due to the uncertainty of spacecraft position and pointing, there will be offsets between overlapping point clouds. In our method, the point cloud is first projected onto a plane, and then the keypoints are extracted using the SIFT algorithm. Finally, we perform coarse and global adjustments based on keypoints. However, low accuracy of the corresponding keypoints will lead to bad registration. In order to improve the accuracy of keypoints matching and point cloud registration, we use the tuple test and RANSAC algorithm to eliminate mismatched points. For the overlapping point clouds of two frames, the RMSE between keypoints is about 0.04m after registration. The results show that this method can improve the accuracy of point cloud registration to a certain extent and meet the application requirements.
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