GMSRI: A Texture-Based Martian Surface Rock Image Dataset
CNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is aff...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/16/5410 |
| _version_ | 1797522096221323264 |
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| author | Cong Wang Zian Zhang Yongqiang Zhang Rui Tian Mingli Ding |
| author_facet | Cong Wang Zian Zhang Yongqiang Zhang Rui Tian Mingli Ding |
| author_sort | Cong Wang |
| collection | DOAJ |
| description | CNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is affected. In this paper, we introduce a new dataset called “GMSRI” that is a mixture of real Mars images and synthetic counterparts which are generated by GAN. GMSRI aims to provide a set of Martian rock images sorted by the texture and spatial structure of rocks. This paper offers a detailed analysis of GMSRI in its current state: Five sub-trees with 28 leaf nodes and 30,000 images in total. We show that GMSRI is much larger in scale and diversity than the current same kinds of datasets. Constructing such a database is a challenging task, and we describe the data collection, selection and generation processes carefully in this paper. Moreover, we evaluate the effectiveness of the GMSRI by an image super-resolution task. We hope that the scale, diversity and hierarchical structure of GMSRI can offer opportunities to researchers in the Mars exploration community and beyond. |
| first_indexed | 2024-03-10T08:24:39Z |
| format | Article |
| id | doaj.art-ab33246c5f794dc8a70563b1884a041f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T08:24:39Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab33246c5f794dc8a70563b1884a041f2023-11-22T09:39:17ZengMDPI AGSensors1424-82202021-08-012116541010.3390/s21165410GMSRI: A Texture-Based Martian Surface Rock Image DatasetCong Wang0Zian Zhang1Yongqiang Zhang2Rui Tian3Mingli Ding4School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaCNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is affected. In this paper, we introduce a new dataset called “GMSRI” that is a mixture of real Mars images and synthetic counterparts which are generated by GAN. GMSRI aims to provide a set of Martian rock images sorted by the texture and spatial structure of rocks. This paper offers a detailed analysis of GMSRI in its current state: Five sub-trees with 28 leaf nodes and 30,000 images in total. We show that GMSRI is much larger in scale and diversity than the current same kinds of datasets. Constructing such a database is a challenging task, and we describe the data collection, selection and generation processes carefully in this paper. Moreover, we evaluate the effectiveness of the GMSRI by an image super-resolution task. We hope that the scale, diversity and hierarchical structure of GMSRI can offer opportunities to researchers in the Mars exploration community and beyond.https://www.mdpi.com/1424-8220/21/16/5410Mars image datasetMartian surface rock imagegenerative adversarial network |
| spellingShingle | Cong Wang Zian Zhang Yongqiang Zhang Rui Tian Mingli Ding GMSRI: A Texture-Based Martian Surface Rock Image Dataset Sensors Mars image dataset Martian surface rock image generative adversarial network |
| title | GMSRI: A Texture-Based Martian Surface Rock Image Dataset |
| title_full | GMSRI: A Texture-Based Martian Surface Rock Image Dataset |
| title_fullStr | GMSRI: A Texture-Based Martian Surface Rock Image Dataset |
| title_full_unstemmed | GMSRI: A Texture-Based Martian Surface Rock Image Dataset |
| title_short | GMSRI: A Texture-Based Martian Surface Rock Image Dataset |
| title_sort | gmsri a texture based martian surface rock image dataset |
| topic | Mars image dataset Martian surface rock image generative adversarial network |
| url | https://www.mdpi.com/1424-8220/21/16/5410 |
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