Reconstructing the landing trajectory of the CE-3 lunar probe by using images from the landing camera

An accurate determination of the landing trajectory of Chang＇e-3 （CE-3） is significant for verifying orbital control strategy, optimizing orbital planning, accu- rately determining the landing site of CE-3 and analyzing the geological background of the landing site. Due to complexities involved in the landing process, there are some differences between the planned trajectory and the actual trajectory of CE-3. The land- ing camera on CE-3 recorded a sequence of the landing process with a frequency of 10 frames per second. These images recorded by the landing camera and high-resolution images of the lunar surface are utilized to calculate the position of the probe, so as to reconstruct its precise trajectory. This paper proposes using the method of trajectory reconstruction by Single Image Space Resection to make a detailed study of the hov- ering stage at a height of 100 m above the lunar surface. Analysis of the data shows that the closer CE-3 came to the lunar surface, the higher the spatial resolution of im- ages that were acquired became, and the more accurately the horizontal and vertical position of CE-3 could be determined. The horizontal and vertical accuracies were 7.09 m and 4.27 m respectively during the hovering stage at a height of 100.02 m. The reconstructed trajectory can reflect the change in CE-3＇s position during the powered descent process. A slight movement in CE-3 during the hovering stage is also clearly demonstrated. These results will provide a basis for analysis of orbit control strategy, and it will be conducive to adjustment and optimization of orbit control strategy in follow-up missions.

A new segmentation algorithm for lunar surface terrain based on CCD images

Terrain classification is one of the critical steps used in lunar geomorphologic analysis and landing site selection. Most of the published works have focused on a Digital Elevation Model （DEM） to distinguish different regions of lunar terrain. This paper presents an algorithm that can be applied to lunar CCD images by blocking and clustering according to image features, which can accurately distinguish between lunar highland and lunar mare. The new algorithm, compared with the traditional algo- rithm, can improve classification accuracy. The new algorithm incorporates two new features and one Tamura texture feature. The new features are generating an enhanced image histogram and modeling the properties of light reflection, which can represent the geological characteristics based on CCD gray level images. These features are ap- plied to identify texture in order to perform image clustering and segmentation by a weighted Euclidean distance to distinguish between lunar mare and lunar highlands. The new algorithm has been tested on Chang＇e-1 CCD data and the testing result has been compared with geological data published by the U.S. Geological Survey. The result has shown that the algorithm can effectively distinguish the lunar mare from highlands in CCD images. The overall accuracy of the proposed algorithm is satisfactory, and the Kappa coefficient is 0.802, which is higher than the result of combining the DEM with CCD images.

A new lunar absolute control point: established by images from the landing camera on Chang'e-3

The establishment of a lunar control network is one of the core tasks in selenodesy, in which defining an absolute control point on the Moon is the most im- portant step. However, up to now, the number of absolute control points has been very sparse. These absolute control points have mainly been lunar laser ranging retrore- flectors, whose geographical location can be observed by observations on Earth and also identified in high resolution lunar satellite images. The Chang＇e-3 （CE-3） probe successfully landed on the Moon, and its geographical location has been monitored by an observing station on Earth. Since its positional accuracy is expected to reach the meter level, the CE-3 landing site can become a new high precision absolute control point. We use a sequence of images taken from the landing camera, as well as satellite images taken by CE-1 and CE-2, to identify the location of the CE-3 lander. With its geographical location known, the CE-3 landing site can be established as a new abso- lute control point, which will effectively expand the current area of the lunar absolute control network by 22%, and can greatly facilitate future research in the field of lunar surveying and mapping, as well as selenodesy.

Study on the Emotions Conveyed by the Moon Image in Classical Poetry

Image is an abnormal description of a word or phrase used to create artistic conception in classical poem writing.This paper focuses on the literal meaning of image and symbolic meaning of the moon by analyzing typical moon-chanting poems of Li Bai,to illustrate the role of image in ancient poetry writing.

Mapping and compositional analysis of mare basalts in the Aristarchus region of the Moon using Clementine data

The process of accurately defining and outlining mare basalt units is nec- essary for constraining the stratigraphy and ages of basalt units, which are used to determine the duration and the flux of lunar volcanism. We use a combination of Clementine＇s five-band ultraviolet/visible data and Ti02 and FeO abundance distri- bution maps to define homogenous mare basalt units and characterize their composi- tional variations （with maturity） in the Aristarchus region. With 20 groups of distinct mare basaltic soils identified using the method in this paper, six additional spectrally defined areas and five basaltic units are constructed, and their mineralogic quanfiza- tion values provide new constraints on their temporal and spatial evolution. Our results indicate that the Aristarchus region has diverse basalt units and a complex history of volcanic evolution. We also demonstrate that the techniques, from which spectrally distinct mare basalts can be mapped, performed well in this study and can be confi- dently expanded to other mare regions of the Moon.

A method and results of color calibration for the Chang'e-3 terrain camera and panoramic camera

The terrain camera （TCAM） and panoramic camera （PCAM） are two of the major scientific payloads installed on the lander and rover of the Chang＇e 3 mission re- spectively. They both use a Bayer color filter array covering CMOS sensor to capture color images of the Moon＇s surface. RGB values of the original images are related to these two kinds of cameras. There is an obvious color difference compared with human visual perception. This paper follows standards published by the International Commission on Illumination to establish a color correction model, designs the ground calibration experiment and obtains the color correction coefficient. The image qual- ity has been significantly improved and there is no obvious color difference in the corrected images. Ground experimental results show that： （1） Compared with uncor- rected images, the average color difference of TCAM is 4.30, which has been reduced by 62.1%. （2） The average color differences of the left and right cameras in PCAM are 4.14 and 4.16, which have been reduced by 68.3% and 67.6% respectively.

Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Due to the low spatial resolution of images taken from the Chang＇e-1 （CE-I） orbiter, the details of the lunar surface are blurred and lost. Considering the limited spatial resolution of image data obtained by a CCD camera on CE-1, an example-based super-resolution （SR） algorithm is employed to obtain high- resolution （HR） images. SR reconstruction is important for the application of image data to increase the resolution of images. In this article, a novel example-based algorithm is proposed to implement SR reconstruction by single-image analysis, and the computational cost is reduced compared to other example-based SR methods. The results show that this method can enhance the resolution of images using SR and recover detailed information about the lunar surface. Thus it can be used for surveying HR terrain and geological features. Moreover, the algorithm is significant for the HR processing of remotely sensed images obtained by other imaging systems.