A New Global Color Image Dataset and Reference Frame for Mars by Tianwen-
crossref（2024）
Abstract
Global-scale Mars remote-sensing image datasets with accurate and consistent spatial positions contain a wealth of information on its surface morphology, topography, and geological structure. These data are fundamental for scientific research and exploration missions of Mars. Prior to China's first Mars exploration mission (Tianwen-1), none of the available global color-image maps of Mars with a spatial resolution of hundreds of meters were true-color products. On the other hand, there is currently a lack of global optical image datasets on a scale of several tens of meters with high-precision positioning and consistency that can be served as a reference frame for Mars. Global remote sensing of Mars is one of the primary scientific goals of Tianwen-1. As of July 25, 2022, The Moderate Resolution Imaging Camera (MoRIC) onboard the orbiter has obtained 14,757 images, which have allowed acquiring global stereo images of the entire Martian surface. Additionally, the Mars Mineralogical Spectrometer (MMS) has returned 325 strips of visible and near-infrared spectral measurement data. These measurement data have laid the foundation for the development of a high-resolution global color-image map of Mars with high positioning accuracy and internal consistency. After processing of radiometric calibration (atmospheric correction, photometric correction and color correction), geometric correction (global adjustments and orthorectification) and global image cartography (global color uniformity, mosaicking and subdivision), the development of the Tianwen-1 Mars Global Color Orthomosaic and datasets based on these data was completed, with a spatial resolution of 76m and a planar position accuracy of 68m (a root mean square (RMS) residual of 0.9 pixels for tie points). This is currently the highest resolution global true color image map of Mars in the world, which can be served as a new Mars geodetic control network and reference frame. It can provide crucial foundational data for Mars scientific research and engineering implementation.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined