The Mastcam multispectral imagers onboard the Mars rover Curiosity have been collecting data since 2012. There are two imagers onboard the rover. The left imager has a wide field of view, but three times lower resolution than that of the right, which is just the opposite. Left and right images can be combined to generate stereo and disparity images. However, the resolution of the stereo images using conventional ways is at the same resolution of the left. Ideally, it will be more interesting to science fans and rover operators if one can generate stereo images with the same resolution of the right imager, as the resolution will be three times better. Recently, we have developed some algorithms that can fuse left and right images to create left images with the same resolution of the right. Consequently, high resolution stereo images can be generated. Moreover, disparity images can also be generated. In this document, we will summarize the development of new JMARS layers that display the enhanced left images using pansharpening and deep learning algorithms, high resolution stereo images, and high resolution disparity maps. The details of the workflow will be described. Some demonstration examples will be given as well.
It is commonly believed that having more white pixels in a color filter array (CFA) will help the demosaicing performance for images collected in low lighting conditions. We present a comparative study to evaluate the performance of demosaicing for images collected in realistic low lighting conditions using two CFAs: the standard Bayer pattern (aka CFA 1.0) and the Kodak CFA 2.0 (RGBW pattern with 50% white pixels). Using a data set containing 10 images collected in low lighting conditions, we observe that having more white pixels does help the demosaicing performance. However, some cautions are needed in quantifying the performance.
The Mastcam multispectral imagers onboard the Mars rover Curiosity have been collecting data since 2012. There are two imagers. The left imager has wide field of view, but three times lower resolution than that of the right, which is just the opposite. Left and right images can be combined to generate stereo images. However, the resolution of the stereo images using conventional ways is at the same resolution of the left. Ideally, it will be more interesting to science fans and rover operators if one can generate stereo images with the same resolution of the right imager, as the resolution will be three times better. Recently, we have developed some algorithms that can fuse left and right images to create left images with the same resolution of the right. Consequently, high resolution stereo images can be generated. Moreover, disparity image can also be generated. In this paper, we will summarize the development of a data processing pipeline that takes left and right Mastcam images from the Planetary Data System (PDS) archive, performs pansharpening to enhance the left images with help from the right images, generates high resolution stereo images, disparity maps, and saves the processed images back into the PDS archive. The details of the workflow will be described. For example, image alignment algorithm, the pansharpening algorithm, stereo image formation algorithms, and disparity map generation algorithms will be summarized. Some demonstration examples will be given as well.
In the 2015 NASA ROSES solicitation, NASA has expressed strong interest in improving the accuracy of Mars surface characterization using satellite images. Thermal Emission Imaging System (THEMIS), an imager with a spatial resolution of 100 meters, has 10 infrared bands between 6 and 15 micrometers. Thermal Emission Spectrometer (TES), an imager with a spatial resolution of 3 km, has 143 bands between 5 and 50 micrometers. While both imagers have a variety of applications, it would be ideal to generate high-spatial and high-spectral resolution data products by fusing their respective outputs. We present a novel approach to fusing THEMIS and TES satellite images, aiming to improve orbital characterization of Mars’ surface. First, the THEMIS bands must undergo atmospheric compensation (AC) due to the presence of dust, ice, carbon dioxide, etc. A systematic AC procedure using elevation information and spectrally uniform pixels has been developed and implemented. Second, a set of proven pan-sharpening algorithms has been applied to fuse the two sets of images. The pan-sharpened images have the spatial resolution of THEMIS images and the spectral resolution of TES images. The results of extensive experiments using THEMIS and TES data collected near the Syrtis Major region (one of the final 3 candidate landing sites for the Mars 2020 rover) clearly demonstrate the feasibility of the proposed approach.
NASA has been planning a hyperspectral infrared imager mission which will provide global coverage using a hyperspectral imager with 60-m resolution. In some practical applications, such as special crop monitoring or mineral mapping, 60-m resolution may still be too coarse. There have been many pansharpening algorithms for hyperspectral images by fusing high-resolution (HR) panchromatic or multispectral images with low-resolution (LR) hyperspectral images. We propose an approach to generating HR hyperspectral images by fusing high spatial resolution color images with low spatial resolution hyperspectral images. The idea is called hybrid color mapping (HCM) and involves a mapping between a high spatial resolution color image and a low spatial resolution hyperspectral image. Several variants of the color mapping idea, including global, local, and hybrid, are proposed and investigated. It was found that the local HCM yielded the best performance. Comparison of the local HCM with >10 state-of-the-art algorithms using five performance metrics has been carried out using actual images from the air force and NASA. Although our HCM method does not require a point spread function (PSF), our results are comparable to or better than those methods that do require PSF. More importantly, our performance is better than most if not all methods that do not require PSF. After applying our HCM algorithm, not only the visual performance of the hyperspectral image has been significantly improved, but the target classification performance has also been improved. Another advantage of our technique is that it is very efficient and can be easily parallelized. Hence, our algorithm is very suitable for real-time applications.
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