{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:04:00Z","timestamp":1760241840374,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T00:00:00Z","timestamp":1537833600000},"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 the course of the TerraSAR-X mission, various new applications based on X-Band Synthetic Aperture Radar (SAR) data have been developed and made available as operational products or services. In this article, we elaborate on proven characteristics of TerraSAR-X that are responsible for development of operational applications. This article is written from the perspective of a commercial data and service provider and the focus is on the following applications with high commercial relevance, and varying operational maturity levels: Surface Movement Monitoring (SMM), Ground Control Point (GCP) extraction and Automatic Target Recognition (ATR). Based on these applications, the article highlights the successful transition of innovative research into sustainable and operational use within various market segments. TerraSAR-X\u2019s high orbit accuracy, its precise radar beam tracing, the high-resolution modes, and high-quality radiometric performance have proven to be the instrument\u2019s advanced characteristics, through, which reliable ground control points and surface movement measurements are obtained. Moreover, TerraSAR-X high-resolution data has been widely exploited for the clarity of its target signatures in the fields of target intelligence and identification. TerraSAR-X\u2019s multi temporal interferometry applications are non-invasive and are now fully standardised autonomous tools to measure surface deformation. In particular, multi-baseline interferometric techniques, such as Persistent Scatter Interferometry (PSI) and Small Baseline Subsets (SBAS) benefit from TerraSAR-X\u2019s highly precise orbit information and phase stability. Similarly, the instrument\u2019s precise orbit information is responsible for sub-metre accuracy of Ground Control Points (GCPs), which are essential inputs for orthorectification of remote sensing imagery, to locate targets, and to precisely georeference a variety of datasets. While geolocation accuracy is an essential ingredient in the intelligence field, high-resolution TerraSAR-X data, particularly in Staring SpotLight mode has been widely used in surveillance, security and reconnaissance applications in real-time and also by automatic or assisted target recognition software.<\/jats:p>","DOI":"10.3390\/rs10101535","type":"journal-article","created":{"date-parts":[[2018,9,25]],"date-time":"2018-09-25T11:12:26Z","timestamp":1537873946000},"page":"1535","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Development of Operational Applications for TerraSAR-X"],"prefix":"10.3390","volume":"10","author":[{"given":"Oliver","family":"Lang","sequence":"first","affiliation":[{"name":"Airbus Defence and Space GmbH, Platz der Einheit 14, 14467 Potsdam, Germany"}]},{"given":"Parivash","family":"Lumsdon","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Claude-Dornier Stra\u00dfe, 88090 Immenstaad, Germany"}]},{"given":"Diana","family":"Walter","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Platz der Einheit 14, 14467 Potsdam, Germany"}]},{"given":"Jan","family":"Anderssohn","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Platz der Einheit 14, 14467 Potsdam, Germany"}]},{"given":"Wolfgang","family":"Koppe","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Claude-Dornier Stra\u00dfe, 88090 Immenstaad, Germany"}]},{"given":"J\u00fcergen","family":"Janoth","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Claude-Dornier Stra\u00dfe, 88090 Immenstaad, Germany"}]},{"given":"Tamer","family":"Koban","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Rechliner Str., 85077 Manching, Germany"}]},{"given":"Christoph","family":"Stahl","sequence":"additional","affiliation":[{"name":"Airbus Defence and Space GmbH, Rechliner Str., 85077 Manching, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4043","DOI":"10.1109\/TGRS.2007.906092","article-title":"Urban-Target Recognition by Means of Repeated Spaceborne SAR Images","volume":"45","author":"Perissin","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","unstructured":"(2018, September 12). Airbus Defence and Space Case Studies. Available online: http:\/\/www.intelligence-airbusds.com\/en\/6988-case-study-gallery-details?item=41559&products_services={Case_Study:Products__Services:value}&search=&market=2397&keyword=."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/36.898661","article-title":"Permanent Scatterers in SAR Interferometry","volume":"39","author":"Ferretti","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TGRS.2002.803792","article-title":"A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms","volume":"40","author":"Berardino","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","unstructured":"(2018, September 12). Bodenbewegungsdienst Deutschland (BBD). Available online: https:\/\/www.bgr.bund.de\/DE\/Themen\/Erdbeben-Gefaehrdungsanalysen\/Fernerkundliche_Gefaehrdungsanalysen\/fernerkundliche_gefaehrdungsanalysen_node.html."},{"key":"ref_6","unstructured":"Harris Geospatial Solutions (2018, July 31). SARscape Help Manual. Available online: https:\/\/www.harrisgeospatial.com\/docs\/pdf\/sarscape_5.1_help.pdf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1109\/TGRS.2010.2060264","article-title":"Imaging Geodesy\u2014Towards Centimeter-Level Ranging Accuracy with TerraSAR-X","volume":"49","author":"Eineder","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","unstructured":"Fritz, T., and Eineder, M. (2014, December 12). TerraSAR-X Basic Product Specification Document. Available online: http:\/\/www.intelligence-airbusds.com\/en\/228-terrasar-x-technical-documents."},{"key":"ref_9","unstructured":"Raggam, H., and Almer, A. (1990, January 20\u201324). Mathematical Aspects of Multi-Sensor Stereo Mapping. Proceedings of the 1990 IEEE International Geoscience and Remote Sensing Symposium (IGRASS): Remote Sensing\u2014Science for the Nineties, Washington, DC, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Koppe, W., Wenzel, R., Hennig, S., Janoth, J., Hummel, P., and Raggam, H. (2012, January 22\u201327). Quality assessment of TerraSAR-X derived ground control points. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGRASS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6350643"},{"key":"ref_11","unstructured":"Raggam, H., Perko, R., Gutjahr, K.H., Koppe, W., Kiefl, N., and Hennig, S. (2010, January 7\u201310). Accuracy Assessment of 3D Point Retrieval from TerraSAR-X Data Sets. Proceedings of the 2010 EUSAR European Conference on Synthetic Aperture Radar, Aachen, Germany."},{"key":"ref_12","unstructured":"Hummel, P. (2014). Remotely Sensed Ground Control Points, Compass Data Inc.. Available online: http:\/\/www.compassdatainc.com."},{"key":"ref_13","unstructured":"Koppe, W., Hennig, S., and Henrichs, L. (2015, January 16\u201318). 3D Point Measurement from Space Using TerraSAR-X HS and ST Stereo Imagery. Proceedings of the DGPF (German Society for Photogrammetry, Remote Sensing and Geoinformation) Conference, K\u00f6ln, Germany."},{"key":"ref_14","unstructured":"Mizell, E., and Biery, R. (2017). Introduction to GPUs for Data Analytics Advances and Applications for Accelerated Computing, O\u2019Reilly Media, Inc.. Available online: https:\/\/www.networld.co.jp\/files\/9615\/0846\/8069\/GPUs_Data_Analytics_Book.pdf."},{"key":"ref_15","unstructured":"Dertat, A. (2018, June 04). Applied Deep Learning\u2014Part 4: Convolutional Neural Networks, @ Pinterest Nov 8, 2017. Available online: https:\/\/towardsdatascience.com\/applied-deep-learning-part-4-convolutional-neural-networks-584bc134c1e2."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, Z., Pan, Z., and Lei, B. (2017). Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sens., 9.","DOI":"10.3390\/rs9090907"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kroll, C., von der Werth, M., Leuck, H., Stahl, C., and Schertler, K. (2017, January 1). Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions. Proceedings of the SPIE, Automatic Target Recognition XXVII, Anaheim, CA, USA.","DOI":"10.1117\/12.2262064"},{"key":"ref_18","unstructured":"Schertler, K., and Liebelt, J. (2016). Automatic Learning Method for the Automatic Learning of Forms of Appearance of Objects in Images. (No. 9361543B2), U.S. Patent."},{"key":"ref_19","unstructured":"Evers, C.M. (2011). Novel Techniques for Enhancing SAR Imaging Using Spatially Variant Apodization. [Master\u2019s Thesis, The Graduate School of The Ohio State University]."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Novak, L.M., and Hesse, S.R. (1992, January 16). Optimal polarizations for radar detection and recognition of targets in clutter. Proceedings of the Automatic Object Recognition II, Aerospace Sensing, Orlando, FL, USA.","DOI":"10.1117\/12.138261"},{"key":"ref_21","unstructured":"(2015). TerraSAR-X Image Product Guide, Basic and Enhanced Radar Satellite Imagery, Airbus Defence and Space."},{"key":"ref_22","unstructured":"Huang, F.J., and LeCun, Y. (2006, January 17\u201322). Large-scale learning with SVM and convolutional nets for generic object categorization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York, NY, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1535\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:22:15Z","timestamp":1760196135000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/10\/1535"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,9,25]]},"references-count":22,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["rs10101535"],"URL":"https:\/\/doi.org\/10.3390\/rs10101535","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,9,25]]}}}