{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:48:03Z","timestamp":1770742083906,"version":"3.49.0"},"reference-count":28,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T00:00:00Z","timestamp":1663200000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DTM) and thus obtaining a 3D spatial mapping, which is necessary for a better analysis of planetary surfaces. However, the entire reconstruction process performed with the stereo-matching algorithm can be time consuming and can generate many artifacts. Coupled with the lack of adequate stereo coverage, it can pose a significant obstacle to 3D planetary mapping. Recently, many deep learning architectures have been proposed for monocular depth estimation, which aspires to predict the third dimension given a single 2D image, with considerable advantages thanks to the simplification of the reconstruction problem, leading to a significant increase in interest in deep models for the generation of super-resolution images and DTM estimation. In this paper, we combine these last two concepts into a single end-to-end model and introduce a new generative adversarial network solution that estimates the DTM at 4\u00d7 resolution from a single monocular image, called SRDiNet (super-resolution depth image network). Furthermore, we introduce a sub-network able to apply a refinement using interpolated input images to better enhance the fine details of the final product, and we demonstrate the effectiveness of its benefits through three different versions of the proposal: SRDiNet with GAN approach, SRDiNet without adversarial network, and SRDiNet without the refinement learned network plus GAN approach. The results of Oxia Planum (the landing site of the European Space Agency\u2019s Rosalind Franklin ExoMars rover 2023) are reported, applying the best model along all Oxia Planum tiles and releasing a 3D product enhanced by 4\u00d7.<\/jats:p>","DOI":"10.3390\/rs14184619","type":"journal-article","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T01:35:10Z","timestamp":1663292110000},"page":"4619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Adversarial Generative Network Designed for High-Resolution Monocular Depth Estimation from 2D HiRISE Images of Mars"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4355-0366","authenticated-orcid":false,"given":"Riccardo","family":"La Grassa","sequence":"first","affiliation":[{"name":"INAF-Astronomical Observatory Padua, 35100 Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7076-8328","authenticated-orcid":false,"given":"Ignazio","family":"Gallo","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9929-2995","authenticated-orcid":false,"given":"Cristina","family":"Re","sequence":"additional","affiliation":[{"name":"INAF-Astronomical Observatory Padua, 35100 Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9021-1140","authenticated-orcid":false,"given":"Gabriele","family":"Cremonese","sequence":"additional","affiliation":[{"name":"INAF-Astronomical Observatory Padua, 35100 Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0565-7496","authenticated-orcid":false,"given":"Nicola","family":"Landro","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy"}]},{"given":"Claudio","family":"Pernechele","sequence":"additional","affiliation":[{"name":"INAF-Astronomical Observatory Padua, 35100 Padua, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5993-0868","authenticated-orcid":false,"given":"Emanuele","family":"Simioni","sequence":"additional","affiliation":[{"name":"INAF-Astronomical Observatory Padua, 35100 Padua, Italy"}]},{"given":"Mattia","family":"Gatti","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Science, University of Insubria, 21100 Varese, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107112","DOI":"10.1016\/j.patcog.2019.107112","article-title":"Depth-map completion for large indoor scene reconstruction","volume":"99","author":"Liu","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1023\/A:1014573219977","article-title":"A taxonomy and evaluation of dense two-frame stereo correspondence algorithms","volume":"47","author":"Scharstein","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_3","unstructured":"F\u00f6rstner, W. (1986). A feature based correspondence algorithm for image matching. ISPRS ComIII Rovan., 150\u2013166."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1111\/j.1477-9730.1984.tb00505.x","article-title":"Digital image correlation: Performance and potential application in photogrammetry","volume":"11","author":"Ackermann","year":"1984","journal-title":"Photogramm. Rec."},{"key":"ref_5","unstructured":"Krystek, P. (1991). Fully automatic measurement of digital elevation models with MATCH-T. Proceedings of the 43th Photogrammetric Week, Stuttgart, Germany, 9\u201314 September 1991, Institus f\u00fcr Photogrammetrie der Universit\u00e4t Stuttgart."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105165","DOI":"10.1016\/j.pss.2021.105165","article-title":"3DPD: A photogrammetric pipeline for a PUSH frame stereo cameras","volume":"198","author":"Simioni","year":"2021","journal-title":"Planet. Space Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105515","DOI":"10.1016\/j.pss.2022.105515","article-title":"CaSSIS-based stereo products for Mars after three years in orbit","volume":"219","author":"Re","year":"2022","journal-title":"Planet. Space Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neucom.2020.12.089","article-title":"Deep learning for monocular depth estimation: A review","volume":"438","author":"Ming","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tao, Y., Xiong, S., Conway, S.J., Muller, J.P., Guimpier, A., Fawdon, P., Thomas, N., and Cremonese, G. (2021). Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets. Remote Sens., 13.","DOI":"10.3390\/rs13152877"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gwn Lore, K., Reddy, K., Giering, M., and Bernal, E.A. (2018, January 18\u201322). Generative adversarial networks for depth map estimation from RGB video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00163"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tao, Y., Muller, J.P., Xiong, S., and Conway, S.J. (2021). MADNet 2.0: Pixel-Scale Topography Retrieval from Single-View Orbital Imagery of Mars Using Deep Learning. Remote Sens., 13.","DOI":"10.3390\/rs13214220"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Fu, H., Gong, M., Wang, C., Batmanghelich, K., and Tao, D. (2018, January 18\u201322). Deep ordinal regression network for monocular depth estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00214"},{"key":"ref_13","unstructured":"Bhat, S.F., Alhashim, I., and Wonka, P. (2021, January 19\u201325). Adabins: Depth estimation using adaptive bins. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual Conference."},{"key":"ref_14","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., Xie, L., Dong, C., and Shan, Y. (2021, January 11\u201317). Real-esrgan: Training real-world blind super-resolution with pure synthetic data. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00217"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4027","DOI":"10.1109\/TIP.2020.2970248","article-title":"Learned image downscaling for upscaling using content adaptive resampler","volume":"29","author":"Sun","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","unstructured":"Kim, D., Ga, W., Ahn, P., Joo, D., Chun, S., and Kim, J. (2022). Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"McEwen, A.S., Eliason, E.M., Bergstrom, J.W., Bridges, N.T., Hansen, C.J., Delamere, W.A., Grant, J.A., Gulick, V.C., Herkenhoff, K.E., and Keszthelyi, L. (2007). Mars reconnaissance orbiter\u2019s high resolution imaging science experiment (HiRISE). J. Geophys. Res. Planets, 112.","DOI":"10.1029\/2005JE002605"},{"key":"ref_19","unstructured":"(2022, March 01). HiRISE Repository. Available online: https:\/\/www.uahirise.org\/dtm\/."},{"key":"ref_20","unstructured":"Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., and Shao, L. (2020). Normalization techniques in training dnns: Methodology, analysis and application. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"LeCun, Y.A., Bottou, L., Orr, G.B., and M\u00fcller, K.R. (2012). Efficient backprop. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_3"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201322). Residual dense network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_24","first-page":"100004","article-title":"Generative adversarial network: An overview of theory and applications","volume":"1","author":"Aggarwal","year":"2021","journal-title":"Int. J. Inf. Manag. Data Insights"},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_26","unstructured":"Loshchilov, I., and Hutter, F. (2016). Sgdr: Stochastic gradient descent with warm restarts. arXiv."},{"key":"ref_27","unstructured":"Source, H.O. (2022, March 01). HiRISE, Oxia Planum Site. Available online: https:\/\/www.uahirise.org\/dtm\/ESP_037070_1985."},{"key":"ref_28","unstructured":"La Grassa, R. (2022, March 01). Pytorch Code SRDiNet 2022. Available online: https:\/\/gitlab.com\/riccardo2468\/srdinet."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4619\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:32:27Z","timestamp":1760142747000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/18\/4619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,15]]},"references-count":28,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14184619"],"URL":"https:\/\/doi.org\/10.3390\/rs14184619","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,15]]}}}