{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:51:00Z","timestamp":1775472660989,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,13]],"date-time":"2021-12-13T00:00:00Z","timestamp":1639353600000},"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>SAR tomography (TomoSAR) is an important technology for three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. In order to obtain sufficient signal-to-noise ratio (SNR), typical TomoSAR applications often require dozens of scenes of SAR images. However, limited by time and cost, the available SAR images are often only 3\u20135 scenes in practice, which makes the traditional TomoSAR technique unable to produce satisfactory SNR and elevation resolution. To tackle this problem, the conditional generative adversarial network (CGAN) is proposed to improve the TomoSAR 3D reconstruction by learning the prior information of building. Moreover, the number of tracks required can be reduced to three. Firstly, a TomoSAR 3D super-resolution dataset is constructed using high-quality data from the airborne array and low-quality data obtained from a small amount of tracks sampled from all observations. Then, the CGAN model is trained to estimate the corresponding high-quality result from the low-quality input. Airborne data experiments prove that the reconstruction results are improved in areas with and without overlap, both qualitatively and quantitatively. Furthermore, the network pretrained on the airborne dataset is directly used to process the spaceborne dataset without any tuning, and generates satisfactory results, proving the effectiveness and robustness of our method. The comparative experiment with nonlocal algorithm also shows that the proposed method has better height estimation and higher time efficiency.<\/jats:p>","DOI":"10.3390\/rs13245055","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T01:22:05Z","timestamp":1639444925000},"page":"5055","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["TomoSAR 3D Reconstruction for Buildings Using Very Few Tracks of Observation: A Conditional Generative Adversarial Network Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8963-2458","authenticated-orcid":false,"given":"Shihong","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2272-0450","authenticated-orcid":false,"given":"Jiayi","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yueting","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yuxin","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chibiao","family":"Ding","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yirong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/36.868873","article-title":"First Demonstration of Airborne SAR Tomography Using Multibaseline L-Band Data","volume":"38","author":"Reigber","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3648","DOI":"10.1109\/TGRS.2011.2125972","article-title":"Analyzing Tomographic SAR Data of a Forest With Respect to Frequency, Polarization, and Focusing Technique","volume":"49","author":"Frey","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TGRS.2010.2050487","article-title":"Very High Resolution Spaceborne SAR Tomography in Urban Environment","volume":"48","author":"Zhu","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8026","DOI":"10.1109\/TGRS.2020.2986052","article-title":"SAR Tomography at the Limit: Building Height Reconstruction Using Only 3\u20135 TanDEM-X Bistatic Interferograms","volume":"58","author":"Shi","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/JSTSP.2015.2469646","article-title":"Joint Sparsity in SAR Tomography for Urban Mapping","volume":"9","author":"Zhu","year":"2015","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1109\/JSTARS.2020.2995503","article-title":"Building 3-D Reconstruction With a Small Data Stack Using SAR Tomography","volume":"13","author":"Lu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhou, S., Li, Y., Zhang, F., Chen, L., and Bu, X. (2019, January 11\u201313). Automatic Reconstruction of 3-D Building Structures for TomoSAR Using Neural Networks. Proceedings of the 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China.","DOI":"10.1109\/ICSIDP47821.2019.9173255"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, S., Guo, J., Zhang, Y., Hu, Y., Ding, C., and Wu, Y. (2021). Single Target SAR 3D Reconstruction Based on Deep Learning. Sensors, 21.","DOI":"10.3390\/s21030964"},{"key":"ref_9","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative Adversarial Networks. arXiv."},{"key":"ref_10","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv."},{"key":"ref_11","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., and Aila, T. (2019). A Style-Based Generator Architecture for Generative Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, W., Huang, Q., You, S., Yang, C., and Neumann, U. (2017). Shape Inpainting Using 3D Generative Adversarial Network and Recurrent Convolutional Networks. arXiv.","DOI":"10.1109\/ICCV.2017.252"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. (2017). StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks. arXiv.","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref_15","unstructured":"Brock, A., Donahue, J., and Simonyan, K. (2018). Large Scale GAN Training for High Fidelity Natural Image Synthesis. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., and Ortega-Garcia, J. (2020). DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. arXiv.","DOI":"10.1016\/j.inffus.2020.06.014"},{"key":"ref_17","unstructured":"Wang, X., Han, S., Chen, Y., Gao, D., and Vasconcelos, N. (2004). Volumetric Attention for 3D Medical Image Segmentation and Detection. arXiv."},{"key":"ref_18","unstructured":"Yu, Q., Xia, Y., Xie, L., Fishman, E.K., and Yuille, A.L. (2019). Thickened 2D Networks for Efficient 3D Medical Image Segmentation. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., and Matas, J. (2018). DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00854"},{"key":"ref_20","unstructured":"Zhu, X.X., and Bamler, R. (2012, January 23\u201326). Super-Resolution of Sparse Reconstruction for Tomographic SAR Imaging\u2014Demonstration with Real Data. Proceedings of the EUSAR 2012 9th European Conference on Synthetic Aperture Radar, Nuremberg, Germany."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhu, X.X., and Bamler, R. (2011, January 13\u201315). Sparse Reconstrcution Techniques for SAR Tomography. Proceedings of the 2011 17th International Conference on Digital Signal Processing (DSP), Corfu, Greece.","DOI":"10.1109\/ICDSP.2011.6005022"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, X.X., Adam, N., and Bamler, R. (2009, January 12\u201317). Space-Borne High Resolution Tomographic Interferometry. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417515"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.isprsjprs.2020.10.013","article-title":"Urban 3D Imaging Using Airborne TomoSAR: Contextual Information-Based Approach in the Statistical Way","volume":"170","author":"Jiao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1109\/MSP.2014.2312073","article-title":"Tomographic Processing of Interferometric SAR Data: Developments, Applications, and Future Research Perspectives","volume":"31","author":"Fornaro","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_25","unstructured":"Xiang, Z.X., and Bamler, R. (2010, January 20\u201330). Compressive Sensing for High Resolution Differential SAR Tomography-the SL1MMER Algorithm. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei\u00df, M., Fornaro, G., and Reale, D. (2015, January 17\u201319). Multi Scatterer Detection within Tomographic SAR Using a Compressive Sensing Approach. Proceedings of the 2015 3rd International Workshop on Compressed Sensing Theory and Its Applications to Radar, Sonar and Remote Sensing (CoSeRa), Pisa, Italy.","DOI":"10.1109\/CoSeRa.2015.7330254"},{"key":"ref_27","unstructured":"Lie-Chen, L., and Dao-Jing, L. (2014). Sparse Array SAR 3D Imaging for Continuous Scene Based on Compressed Sensing. J. Electron. Inf. Technol., 36."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1364\/OL.33.000156","article-title":"Efficient Subpixel Image Registration Algorithms","volume":"33","author":"Thurman","year":"2008","journal-title":"Opt. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., and Efros, A.A. (2018). Image-to-Image Translation with Conditional Adversarial Networks. arXiv.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cozzolino, D., Verdoliva, L., Scarpa, G., and Poggi, G. (August, January 28). Nonlocal Sar Image Despeckling by Convolutional Neural Networks. Proceedings of the IGARSS 2019\u20132019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8897761"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1109\/TGRS.2010.2076376","article-title":"NL-InSAR: Nonlocal Interferogram Estimation","volume":"49","author":"Deledalle","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/TGRS.2017.2746420","article-title":"Nonlocal Filtering Applied to 3-D Reconstruction of Tomographic SAR Data","volume":"56","author":"Guillaso","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3015","DOI":"10.1109\/TGRS.2018.2879382","article-title":"Nonlocal Compressive Sensing-Based SAR Tomography","volume":"57","author":"Shi","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"D\u2019Hondt, O., L\u00f3pez-Mart\u00ednez, C., Guillaso, S., and Hellwich, O. (2017, January 23\u201328). Impact of Non-Local Filtering on 3D Reconstruction from Tomographic SAR Data. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127495"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1109\/TGRS.2018.2854660","article-title":"Multipath Scattering of Typical Structures in Urban Areas","volume":"57","author":"Cheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5055\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:46:53Z","timestamp":1760168813000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5055"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,13]]},"references-count":36,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245055"],"URL":"https:\/\/doi.org\/10.3390\/rs13245055","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,13]]}}}