{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:53:05Z","timestamp":1760230385897,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)","doi-asserted-by":"publisher","award":["269661170"],"award-info":[{"award-number":["269661170"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The instruments of the Mars Reconnaissance Orbiter (MRO) provide a large quantity and variety of imagining data for investigations of the Martian surface. Among others, the hyper-spectral Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) captures visible to infrared reflectance across several hundred spectral bands. However, Mars is only partially covered with targeted CRISM at full spectral and spatial resolution. In fact, less than one percent of the Martian surface is imaged in this way. In contrast, the Context Camera (CTX) onboard the MRO delivers images with a higher spatial resolution and the image data cover almost the entire Martian surface. In this work, we examine to what extent machine learning systems can learn the relation between morphology, albedo and spectral composition. To this end, a dataset of 67 CRISM-CTX image pairs is created and different deep neural networks are trained for the pixel-wise prediction of CRISM bands solely based on the albedo information of a CTX image. The trained models enable us to estimate spectral bands across large areas without existing CRISM data and to predict the spectral composition of any CTX image. The predictions are qualitatively similar to the ground-truth spectra and are also able to recover finer grained details, such as dunes or small craters.<\/jats:p>","DOI":"10.3390\/rs14143457","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:19:21Z","timestamp":1658189961000},"page":"3457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Learning the Link between Albedo and Reflectance: Machine Learning-Based Prediction of Hyperspectral Bands from CTX Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4013-7146","authenticated-orcid":false,"given":"Sergej","family":"Stepcenkov","sequence":"first","affiliation":[{"name":"Image Analysis Group, TU Dortmund University, 44227 Dortmund, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2691-4129","authenticated-orcid":false,"given":"Thorsten","family":"Wilhelm","sequence":"additional","affiliation":[{"name":"Image Analysis Group, TU Dortmund University, 44227 Dortmund, Germany"}]},{"given":"Christian","family":"W\u00f6hler","sequence":"additional","affiliation":[{"name":"Image Analysis Group, TU Dortmund University, 44227 Dortmund, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zurek, R.W., and Smrekar, S.E. (2007). An overview of the Mars Reconnaissance Orbiter (MRO) science mission. J. Geophys. Res. Planets, 112.","DOI":"10.1029\/2006JE002701"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Murchie, S., Arvidson, R., Bedini, P., Beisser, K., Bibring, J.P., Bishop, J., Boldt, J., Cavender, P., Choo, T., and Clancy, R. (2007). Compact reconnaissance imaging spectrometer for Mars (CRISM) on Mars reconnaissance orbiter (MRO). J. Geophys. Res. Planets, 112.","DOI":"10.1029\/2006JE002682"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Malin, M.C., Bell, J.F., Cantor, B.A., Caplinger, M.A., Calvin, W.M., Clancy, R.T., Edgett, K.S., Edwards, L., Haberle, R.M., and James, P.B. (2007). Context camera investigation on board the Mars Reconnaissance Orbiter. J. Geophys. Res. Planets, 112.","DOI":"10.1029\/2006JE002808"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pelkey, S., Mustard, J., Murchie, S., Clancy, R., Wolff, M., Smith, M., Milliken, R., Bibring, J.P., Gendrin, A., and Poulet, F. (2007). CRISM multispectral summary products: Parameterizing mineral diversity on Mars from reflectance. J. Geophys. Res. Planets, 112.","DOI":"10.1029\/2006JE002831"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ko\u00dfmann, D., Wilhelm, T., and Fink, G.A. (2021, January 10\u201315). Towards tackling multi-label imbalances in remote sensing imagery. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412588"},{"key":"ref_6","unstructured":"Gewali, U.B., Monteiro, S.T., and Saber, E. (2018). Machine learning based hyperspectral image analysis: A survey. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e2021GL095341","DOI":"10.1029\/2021GL095341","article-title":"Copernican-Aged (< 200 Ma) Impact Ejecta at the Chang\u2019e-5 Landing Site: Statistical Evidence From Crater Morphology, Morphometry, and Degradation Models","volume":"48","author":"Qian","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wilhelm, T., and W\u00f6hler, C. (2021, January 10\u201315). Uncertainty Guided Recognition of Tiny Craters on the Moon. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413285"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wilhelm, T., Geis, M., P\u00fcttschneider, J., Sievernich, T., Weber, T., Wohlfarth, K., and W\u00f6hler, C. (2020). Domars16k: A diverse dataset for weakly supervised geomorphologic analysis on mars. Remote Sens., 12.","DOI":"10.3390\/rs12233981"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dundar, M., Ehlmann, B.L., and Leask, E.K. (2019). Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero and NE Syrtis. arXiv.","DOI":"10.1002\/essoar.10501294.1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pang, Y., Lin, J., Qin, T., and Chen, Z. (2021). Image-to-Image Translation: Methods and Applications. arXiv.","DOI":"10.1109\/TMM.2021.3109419"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fuentes Reyes, M., Auer, S., Merkle, N., Henry, C., and Schmitt, M. (2019). Sar-to-optical image translation based on conditional generative adversarial networks\u2014Optimization, opportunities and limits. Remote Sens., 11.","DOI":"10.3390\/rs11172067"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/j.1520-6378.1977.tb00104.x","article-title":"The CIE 1976 color-difference formulae","volume":"2","author":"Robertson","year":"1977","journal-title":"Color Res. Appl."},{"key":"ref_15","unstructured":"Jain, A.K. (1989). Fundamentals of Digital Image Processing, Prentice-Hall, Inc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Charpiat, G., Hofmann, M., and Sch\u00f6lkopf, B. (2008, January 12\u201318). Automatic image colorization via multimodal predictions. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88690-7_10"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., and Efros, A.A. (2016, January 11\u201314). Colorful image colorization. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nazeri, K., Ng, E., and Ebrahimi, M. (2018, January 12\u201313). Image colorization using generative adversarial networks. Proceedings of the International Conference on Articulated Motion and Deformable Objects, Palma de Mallorca, Spain.","DOI":"10.1007\/978-3-319-94544-6_9"},{"key":"ref_19","unstructured":"Bhoi, A. (2019). Monocular depth estimation: A survey. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jiao, J., Cao, Y., Song, Y., and Lau, R. (2018, January 8\u201314). Look deeper into depth: Monocular depth estimation with semantic booster and attention-driven loss. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_4"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"512","DOI":"10.2307\/2095465","article-title":"Regression models with ordinal variables","volume":"49","author":"Winship","year":"1984","journal-title":"Am. Sociol. Rev."},{"key":"ref_23","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_27","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning. PMLR, Long Beach, CA, USA."},{"key":"ref_28","unstructured":"Xu, J., Pan, Y., Pan, X., Hoi, S., Yi, Z., and Xu, Z. (2021). RegNet: Self-Regulated Network for Image Classification. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_30","unstructured":"Kirillov, A., He, K., Girshick, R., and Doll\u00e1r, P. (2022, June 14). A Unified Architecture for Instance and Semantic Segmentation. Available online: http:\/\/presentations.cocodataset.org\/COCO17-Stuff-FAIR.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"179656","DOI":"10.1109\/ACCESS.2020.3025372","article-title":"Ma-net: A multi-scale attention network for liver and tumor segmentation","volume":"8","author":"Fan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical image computing and computer-assisted intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.patcog.2018.05.029","article-title":"Monocular depth estimation with hierarchical fusion of dilated cnns and soft-weighted-sum inference","volume":"83","author":"Li","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_36","unstructured":"Dickson, J.L., Kerber, L.A., Fassett, C.I., and Ehlmann, B.L. (2018, January 19\u201323). A Global, Blended CTX Mosaic of Mars with Vectorized Seam Mapping: A New Mosaicking Pipeline Using Principles of Non-Destructive Image Editing. Proceedings of the 49th Lunar and Planetary Science Conference, The Woodlands, TX, USA."},{"key":"ref_37","unstructured":"Mouginis-Mark, P.J., and Garbeil, H. (2019, January 18\u201322). CTX Digital Elevation Models Facilitate Geomorphic Analysis of Mars. Proceedings of the 50th Lunar and Planetary Science Conference, The Woodlands, TX, USA."},{"key":"ref_38","unstructured":"Boain, R.J. (2004, January 8\u201312). AB-Cs of Sun-Synchronous Orbit Mission Design. Proceedings of the 2004 In 14th AAS\/AIAA Space Flight Mechanics Meeting, Maui, HI, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Munappy, A., Bosch, J., Olsson, H.H., Arpteg, A., and Brinne, B. (2019, January 28\u201330). Data management challenges for deep learning. Proceedings of the 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Kallithea-Chalkidiki, Greece.","DOI":"10.1109\/SEAA.2019.00030"},{"key":"ref_40","unstructured":"Bennett, K.J., Wang, J., and Scholes, D. (2021, January 17\u201321). Accessing PDS Data in Pipeline Processing and Websites Through PDS Geosciences Orbital Data Explorer\u2019s Web-Based API (REST) Interface. Proceedings of the 45th Lunar and Planetary Science Conference, The Woodlands, TX, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.compbiomed.2019.05.002","article-title":"Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm","volume":"109","author":"Buda","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_43","unstructured":"Lin, M., Chen, H., Sun, X., Qian, Q., Li, H., and Jin, R. (2020). Neural architecture design for gpu-efficient networks. arXiv."},{"key":"ref_44","unstructured":"Fergason, R., Hare, T., and Laura, J. (2018). HRSC and MOLA Blended Digital Elevation Model at 200 m v2, Astrogeology PDS Annex, US Geological Survey."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1002\/2014JE004627","article-title":"Revised CRISM spectral parameters and summary products based on the currently detected mineral diversity on Mars","volume":"119","author":"Seelos","year":"2014","journal-title":"J. Geophys. Res. Planets"},{"key":"ref_46","unstructured":"Sucharski, T., Mapel, J., Lee, K., Shepherd, M., Ryan Combs, C., and Stapleton, S. (2022, June 14). USGS-Astrogeology\/ISIS3: ISIS4.1.0 Public Release. Available online: https:\/\/zenodo.org\/record\/3780717\/export\/hx#.YtYHU3ZByUk."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.pss.2017.02.009","article-title":"Investigation of boresight offsets and co-registration of HiRISE and CTX imagery for precision Mars topographic mapping","volume":"139","author":"Wang","year":"2017","journal-title":"Planet. Space Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., and Hostert, P. (2017). AROSICS: An Automated and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data. Remote Sens., 9.","DOI":"10.3390\/rs9070676"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_50","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3457\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:53:21Z","timestamp":1760140401000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3457"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,18]]},"references-count":50,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143457"],"URL":"https:\/\/doi.org\/10.3390\/rs14143457","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,7,18]]}}}