{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T06:27:08Z","timestamp":1780986428152,"version":"3.54.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42271450"],"award-info":[{"award-number":["42271450"]}]},{"name":"National Natural Science Foundation of China","award":["41971415"],"award-info":[{"award-number":["41971415"]}]},{"name":"National Natural Science Foundation of China","award":["42071445"],"award-info":[{"award-number":["42071445"]}]},{"name":"National Natural Science Foundation of China","award":["BK20201387"],"award-info":[{"award-number":["BK20201387"]}]},{"name":"National Natural Science Foundation of China","award":["KLSMNR-G202209"],"award-info":[{"award-number":["KLSMNR-G202209"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["42271450"],"award-info":[{"award-number":["42271450"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["41971415"],"award-info":[{"award-number":["41971415"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["42071445"],"award-info":[{"award-number":["42071445"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20201387"],"award-info":[{"award-number":["BK20201387"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["KLSMNR-G202209"],"award-info":[{"award-number":["KLSMNR-G202209"]}]},{"name":"Key Laboratory of Land Satellite Remote-Sensing Applications, Ministry of Natural Resources of the People\u2019s Republic of China","award":["42271450"],"award-info":[{"award-number":["42271450"]}]},{"name":"Key Laboratory of Land Satellite Remote-Sensing Applications, Ministry of Natural Resources of the People\u2019s Republic of China","award":["41971415"],"award-info":[{"award-number":["41971415"]}]},{"name":"Key Laboratory of Land Satellite Remote-Sensing Applications, Ministry of Natural Resources of the People\u2019s Republic of China","award":["42071445"],"award-info":[{"award-number":["42071445"]}]},{"name":"Key Laboratory of Land Satellite Remote-Sensing Applications, Ministry of Natural Resources of the People\u2019s Republic of China","award":["BK20201387"],"award-info":[{"award-number":["BK20201387"]}]},{"name":"Key Laboratory of Land Satellite Remote-Sensing Applications, Ministry of Natural Resources of the People\u2019s Republic of China","award":["KLSMNR-G202209"],"award-info":[{"award-number":["KLSMNR-G202209"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Crater recognition on Mars is of paramount importance for many space science applications, such as accurate planetary surface age dating and geological mapping. Such recognition is achieved by means of various image-processing techniques employing traditional CNNs (convolutional neural networks), which typically suffer from slow convergence and relatively low accuracy. In this paper, we propose a novel CNN, referred to as MC-UNet (Martian Crater U-Net), wherein classical U-Net is employed as the backbone for accurate identification of Martian craters at semantic and instance levels from thermal-emission-imaging-system (THEMIS) daytime infrared images. Compared with classical U-Net, the depth of the layers of MC-UNet is expanded to six, while the maximum number of channels is decreased to one-fourth, thereby making the proposed CNN-based architecture computationally efficient while maintaining a high recognition rate of impact craters on Mars. For enhancing the operation of MC-UNet, we adopt average pooling and embed channel attention into the skip-connection process between the encoder and decoder layers at the same network depth so that large-sized Martian craters can be more accurately recognized. The proposed MC-UNet is adequately trained using 2\u223c32 km radii Martian craters from THEMIS daytime infrared annotated images. For the predicted Martian crater rim pixels, template matching is subsequently used to recognize Martian craters at the instance level. The experimental results indicate that MC-UNet has the potential to recognize Martian craters with a maximum radius of 31.28 km (136 pixels) with a recall of 0.7916 and F1-score of 0.8355. The promising performance shows that the proposed MC-UNet is on par with or even better than other classical CNN architectures, such as U-Net and Crater U-Net.<\/jats:p>","DOI":"10.3390\/rs15010266","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T04:51:22Z","timestamp":1672635082000},"page":"266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["MC-UNet: Martian Crater Segmentation at Semantic and Instance Levels Using U-Net-Based Convolutional Neural Network"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8118-3889","authenticated-orcid":false,"given":"Dong","family":"Chen","sequence":"first","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3332-4699","authenticated-orcid":false,"given":"P. Takis","family":"Mathiopoulos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4070-9415","authenticated-orcid":false,"given":"Zhenxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0245-5933","authenticated-orcid":false,"given":"Jiju","family":"Peethambaran","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computing Science, Saint Mary\u2019s University, Halifax, NS B3P 2M6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-12162-0","article-title":"The impact origin and evolution of Chryse Planitia on Mars revealed by buried craters","volume":"10","author":"Pan","year":"2019","journal-title":"Nat. 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