In the early 21st century, international lunar exploration activities became frequent due to the plan of human return to the Moon. The SMART-1 (European Space Agency, 2003), Kaguya (Japan, 2007), Chang’E-1 (China, 2007), Chandrayaan-1 (India, 2008), LRO (Lunar Reconnaissance Orbiter, the United States, 2009), Chang’E-2 (China, 2010), LADEE (Lunar Atmosphere and Dust Environment Explorer, the United States, 2013) and Chang’E-3 (China, 2013) lunar missions have achieved great success subsequently.
Remote sensing can provide a global view of the composition of the lunar surface. It may be implemented by electromagnetic wave with different wave lengths that include visible light, near infrared, microwave, X/gamma-ray, ultraviolet and so on. The high resolution visible images are the best way of getting this kind of information (Dunkin and Heather, 2000). Nowadays, the CCD (charge couple device) camera is a usual principal payload on the lunar satellite to obtain digital images. The camera specification of Smart-1, Chang’E, Kaguya, Chandrayaan-1 and LRO are shown in Table 1 (Jin et al., 2013).
Mission CCD Camera Spectral range (μm) Spatial resolution (m/pixel) Swath (km) Coverage Smart-1 Micro-camera 0.75, 0.915, 0.96 80 - Local Chang’E-1 SC: stereo camera 0.5-0.75 120 60 100% Chang’E-2 Stereo camera < 10 ≥43 100% (100 km×100 km orbit) 0.45-0.52 Stereo camera < 1.5 ≥6 Local (100 km×15 km orbit) Kaguya TC: terrain camera 0.43-0.85 10 40 92.4% Chandrayaan-1 TMC: terrain mapping camera 0.5-0.75 5 20 Local LRO NAC: (narrow-angle camera) 0.55 0.5 5 55.9% WAC: (wide-angle camera) 0.3-0.68 100 5 100%
Table 1. Camera specification of Smart-1, Chang’E, Kaguya, Chandrayaan-1 and LRO missions
Furthermore, image map plays a significant role in the investigation of the solid planets. Production of the global image map of the Moon has always been one of the most important aspects of lunar exploration and scientific research. It is the basic and direct material to study the surface features of the Moon.
Circling, landing and returning are three stages in China’s Lunar Exploration Program. On October 24, 2007, the first Chinese lunar orbiter Chang’E-1 satellite was launched successfully. In the orbital altitude of 200 km, its three-line array CCD stereo camera can get three planar images in the same time from three different view angles (forward, nadir and backward). The spatial resolution of CCD images is about 120 m/pixel (Zhao et al., 2009). Since the success of Chang’E-1, Chang’E-2 has developed as a technical test probe for the second stage of China’s Lunar Exploration Program. On October 1, 2010, the second Chinese lunar orbiter Chang’E-2 satellite was launched successfully. The first stage mission has been finished from Chang’E-1 and Chang’E-2 lunar orbiter. To obtain 3-dimension (3D) stereo images of the lunar surface and to provide the high resolution stereo images for the lunar surface (specially, the future landing site of Chang’E-3 lunar lander and rover) are one of the primary scientific objectives of Chang’E-1 and Chang’E-2, respectively (Ouyang et al., 2010; Ouyang, 2010).
In 1959, Luna-3 of the Soviet Union (Russia) sent back the first photographs of the far side of the Moon (approximately 70%). Afterward, using these data, the Atlas Obratnoi Storony Luny was published (Akademia et al., 1960). And starting in the late 1960s, five lunar orbiter missions provided an excellent photographic image for 99% coverage (Hansen, 1970). In 1994, the Clementine spacecraft imaged more than 99% of the Moon’s surface at resolution of 100-200 m/pixel. The ultraviolet/visible (UVVIS) camera at near-visible (750 nm) created a global mosaic at a uniform 100 m/pixel resolution (UVVIS 750 nm Basemap) (Eliason et al., 1999). Based on the Clementine images, the Unified Lunar Control Network (ULCN) and the Clementine Lunar Control Network (CLCN), a new general unified lunar control network (ULCN 2005) and lunar topographic model have been finished (Archinal et al., 2006).
Beyond that, recent lunar exploration missions have provided a large amount of new image data. The lunar reconnaissance orbiter camera (LROC) wide angle camera (WAC) provides global imaging of the Moon at a scale of 100 m/pixel and covers the latitude range -79° to 79°, 98.2% of the entire lunar surface. Due to persistent shadows near the poles it is not possible to create a complete stereo based map at the highest latitudes. The lunar orbiter laser altimeter (LOLA) excels at mapping topography at the poles. With the LROC (WAC) and LOLA instrument, scientists can now accurately portray the shape of the entire Moon at high resolution (Scholten et al., 2012; Speyerer et al., 2011).
A global DTM (digital terrain model) can be produced from the Kaguya remote sensing imagery data sets from an optical sensing instrument called the LISM (lunar imager/ spectrometer, including: terrain camera (TC), multi-band imager (MI) and spectral profiler (SP)). The terrain camera applies push-broom mode. It has spatial resolution of 10 m and covered almost the entire lunar surface (Haruyama et al., 2012, 2008).
The 5 meters spatial resolution of the terrain mapping camera (TMC) on Chandrayaan-1, is intended for systematic topographic mapping of the complete lunar surface, generating high resolution 3D maps of the Moon and has provided unprecedented details of lunar topography including those for Apollo 15 and 17 sites (Goswami and Annadurai, 2009; Kumar et al., 2009).
The global 2D mosaic lunar image (about 120 m resolution, 100% coverage) from Chang’E-1 was released on November 12, 2008, and the global 2D mosaic lunar image (about 7 m resolution, 100% coverage) from Chang’E-2 was released on February 6, 2012. They both were produced by Chinese Lunar Exploration Program (Xinhua News Agency, 2012; Li et al., 2010).
For guarantee of registration accuracy, the traditional approach for drawing global lunar image, which needs to manually select two adjacent tracks of CCD images to mosaic a larger image using a certain method step by step, suffers from computation inefficiency and costs a lot of human resources. For example, in the processing of mosaic image of Chang’E-1, the lunar map is divided into 6 mosaic areas (including 4 regions of low and mid latitude, the South Pole and the North Pole). However, after preprocessing, every single track of image data can’t be mosaiced together, because they have no uniform geo-reference. A processing is just to warp all tracks of images and create a single global map without relative position offset. Firstly, geometric matching of the same point in the neighbor images will be checked in detail. The offset of the match points of 90.9% is not more than 4 pixels. In order to correct the position offset, half images data are taken as base images, others are warped with tie points between neighbor images. Then, in the processing of stitching, after drawing the stitching line, the adjacent tracks images are automatically stitched using the image minimum gray level and gradient algorithm. And the mosaic areas are stitched by the stitching line of adjacent images and the automatic color equalization algorithm. At last, the relative geometric positioning precision of the global image is better than 240 m and the absolute geometric positioning precision of Chang’E-1 global image is approximately 100 to 1 500 m (Li et al., 2010, 2009). Because of huge amount of CCD images data with high resolution, it usually takes very long time, one year at least to finish it for example.
To address these problems, using four vertices’ latitudes and longitudes to realize the frame’s pixels, the mosaic of Chang’E-1 CCD image was completed along with the coordinates matching (Wang et al., 2010). By utilizing database technology and CCD positioning data, an automatic seamless stitching method used for 2C level CCD data from Chang’E-1 lunar mission can accelerate the process and minimize the utilization of human resources to produce global lunar map (Ye et al., 2011).
However, the limitation of these methods originates in positioning accuracy of CCD image data. That is, the geometric positioning accuracy of CCD image data is associated with Selenodesy (especially the lunar control network) and the orbital accuracy. On one hand, selenodesy is different from the geodesy. Because the data source of selenodesy is mainly derived from lunar exploration satellite. And the lunar control network is implemented by the LLR (lunar laser ranging) and the VLBI (very long baseline interferometry) observation etc.. In addition, the auxiliary data of DEM (digital elevation model) from the laser altimeter can be used to improve the positioning accuracy of CCD images. Unfortunately, the laser altimeter on the Chang’E-2 did not provide the correct altimeter data. On the other hand, among the various factors, the error of the orbiter and attitude (that is the orbital accuracy) has influence on the positioning accuracy. For instance, generally, in the processing of lunar image data, the orbital data of lunar exploration satellite, which is of low location precision, is utilized to achieve absolute positioning without the surface control points of the Moon (Xia et al., 2012).
In other words, the relative positioning precision needs to be compared with the resolution of the CCD images. The relative positioning precision of Chang’E-1 CCD image ranges from 538-647 to 1 041-1 273 m (that is 4-10 pixels) (Li et al., 2010). But the relative positioning precision of Chang’E-2 CCD image ranges from 0.411 to 2 667.59 m, the offset of most pixels is less than 400 m. It means that the relative error of two corresponding points may reach 60 pixels (Liu et al., 2013).
In this case, this method cannot apply to the Chang’E-2 CCD data directly due to the contradiction of the high spatial resolution of the CCD image and the low positioning accuracy of location coordinates. In this paper, a revised method for automatic stitching of 2C level CCD data from Chang’E-2 lunar mission is proposed. It still uses database technology and CCD positioning data. Database technology is employed to reorganize CCD images in order to manage and make the huge CCD data access and retrieve easily. But the CCD positioning data needs to be calibrated and the relative positioning error needs to be decreased.
The rest of this paper is organized as follows. Section 1 introduces the CCD image data of Chang’E-2. Section 2 provides an overview about proposed method in this paper and gives a description of processing of CCD data. Sections 3 and 4 provide the mosaic method. Section 5 shows the results and analysis of lunar map from 70°N to 70°S. In Section 6, the conclusion and discussion are shown.
Totally, the forward view of CCD data of 335 tracks (307th orbit to 641th orbit) among two view angles has been used for making the global lunar map closer to orthographic effect as soon as possible and reducing the data volume further.
Experimental environment: operation system is Windows 7; computer memory is 28 GB; CPU is Intel i5; number of the computer is only one. Running time is listed in Table 2.
Algorithm step Tools Running time CCD 2C level data extraction Matlab ≈5 h 15 min CCD image of forward view overlap cutting in each orbit Matlab ≈15 h 10 min Calibration of location coordinates and data table partition Matlab ≈10 d 14 h 24 min About 7 m/pixel resolution image reconstruction Matlab ≈36 d 10 h
Table 2. Running time of proposed method in this paper
The lunar surface map from 70°N to 70°S with high spatial resolution of about less than 10 m that achieves the requirement of the original resolution of Chang’E-2. Because there are many shadows in the near poles region and poles, three-fourth global mosaics have been constructed at full and reduced spatial resolution (1/32 times down-resampling) to display in Fig. 10a. Figure 10b shows the Sinus Iridum area and Mare Imbrium area of the Moon (1/8 times down-resampling). Figure 10c shows the surrounding area in the Chang’E-3 landing site of the Mare Imbrium area (1/2 times down-resampling). Figure 10d shows the Chang’E-3 landing site (full spatial resolution). Figure 11 shows two images mosaic (our method and Chinese Lunar Exploration Program) of the Chang’E-3 landing site. The comparison of the same source (Chang’E-2 CCD images, about 7 m resolution) is meaningful. The two mosaic effect are fairly from eyes, except for whether the projection difference or not. Figure 12 shows the Apollo landing site.
Figure 10. Partial mosaic image. (a) Lunar map from 70°N to 70°S; (b) the Sinus Iridum and Mare Imbrium area; (c) the surrounding area in the Chang’E-3 landing site; (d) the Chang’E-3 landing site.
Figure 11. Comparison of the CCD image mosaic of the Chang’E-3 landing site. (a) Mosaic by Chinese Lunar Exploration Program (this image courtesy of National Astronomical Observatories of China); (b) mosaic by our method.
So far, it is highly desirable to provide the user with an estimate how accurate the mosaic actually is. The accuracy evaluation is a big problem. In this paper, the registration process has been used to calibrate the location coordinates in the processing. Hence, an evaluation of quantitative statistics analysis about the registration accuracy can be provided. One of basic methods for measuring the registration accuracy is alignment error measure.
The Euclid distance between the corresponding points is calibrated as the errors evaluation. Figure 13 shows the accuracy by the pie chart. In the figure, there are 89% corresponding pairs in 0-5 pixels error in total. The accuracy of most corresponding points is controlled under 0-1 pixels. Its average relative location accuracy of the adjacent orbits CCD image data is less than 3 pixels. However, the accuracy is influenced by some bad signal of CCD images due to the CCD camera hardware errors and human errors for preprocessing the original data.