{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:11:41Z","timestamp":1772043101283,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T00:00:00Z","timestamp":1639008000000},"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 Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper.<\/jats:p>","DOI":"10.3390\/rs13245010","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T21:46:58Z","timestamp":1639086418000},"page":"5010","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Horst","family":"Hammer","sequence":"first","affiliation":[{"name":"Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, D-76275 Ettlingen, Germany"}]},{"given":"Silvia","family":"Kuny","sequence":"additional","affiliation":[{"name":"Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, D-76275 Ettlingen, Germany"}]},{"given":"Antje","family":"Thiele","sequence":"additional","affiliation":[{"name":"Fraunhofer IOSB, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, D-76275 Ettlingen, Germany"},{"name":"Institute of Photogrammetry and Remote Sensing (IPF), Karlsruhe Institute of Technology KIT, D-76131 Karlsruhe, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"ref_1","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. Geosc. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zink, M., Moreira, A., Bachmann, M., Rizzoli, P., Fritz, T., Hajnsek, I., Krieger, G., and Wessel, B. (2017, January 23\u201328). The global TanDEM-X DEM\u2014A unique data set. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127099"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wolf, D., and Fern\u00e1ndez, J. (2007). An Overview of the Small BAseline Subset Algorithm: A DInSAR Technique for Surface Deformation Analysis. Deformation and Gravity Change: Indicators of Isostasy, Tectonics, Volcanism, and Climate Change, Birkh\u00e4user.","DOI":"10.1007\/978-3-7643-8417-3"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gang, Y. (July, January 29). SAR image rapid co-registration based on RD model and coherence interpolation. Proceedings of the 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services, Fuzhou, China.","DOI":"10.1109\/ICSDM.2011.5969066"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1109\/JSTARS.2015.2421879","article-title":"The TanDEM-X DEM Mosaicking: Fusion of Multiple Acquisitions Using InSAR Quality Parameters","volume":"9","author":"Gruber","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tzouvaras, M., Danezis, C., and Hadjimitsis, D.G. (2020). Small Scale Landslide Detection Using Sentinel-1 Interferometric SAR Coherence. Remote Sens., 12.","DOI":"10.3390\/rs12101560"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jung, J., and Yun, S.-H. (2020). Evaluation of Coherent and Incoherent Landslide Detection Methods Based on Synthetic Aperture Radar for Rapid Response: A Case Study for the 2018 Hokkaido Landslides. Remote Sens., 12.","DOI":"10.3390\/rs12020265"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Washaya, P., Balz, T., and Mohamadi, B. (2018). Coherence Change-Detection with Sentinel-1 for Natural and Anthropogenic Disaster Monitoring in Urban Areas. Remote Sens., 10.","DOI":"10.3390\/rs10071026"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Stephenson, O.L., Kohne, T., Zhan, E., Cahill, B.E., Yun, S.-H., Ross, Z.E., and Simons, M. (2021). Deep Learning-Based Damage Mapping With InSAR Coherence Time Series. IEEE Trans. Geosc. Remote Sens., 1\u201317.","DOI":"10.1109\/TGRS.2021.3084209"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1002\/esp.4309","article-title":"Mapping dune dynamics by InSAR coherence","volume":"43","author":"Havivi","year":"2018","journal-title":"Earth Surf. Process. Landforms"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Cigna, F., and Tapete, D. (2018). Tracking Human-Induced Landscape Disturbance at the Nasca Lines UNESCO World Heritage Site in Peru with COSMO-SkyMed InSAR. Remote Sens., 10.","DOI":"10.3390\/rs10040572"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Malinas, R., Quach, T., and Koch, M.W. (2015, January 8\u201311). Vehicle track detection in CCD imagery via conditional random field. Proceedings of the 49th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2015.7421411"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Y., Wang, B., Hu, X., Song, C., and Xiang, M. (2021). Human Activity Detection Based on Multipass Airborne InSAR Coherence Matrix. IEEE Geosc. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2021.3077614"},{"key":"ref_14","unstructured":"Sosnovsky, A., and Kobernichenko, V. (2015, January 9\u201311). Algorithm of Interferometric Coherence Estimation for Synthetic Aperture Radar Image Pair. Proceedings of the 4th International Conference on Analysis of Images, Social Networks and Texts (AIST), Yekaterinburg, Russia."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.1109\/TGRS.2015.2502219","article-title":"A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection","volume":"54","author":"Wahl","year":"2016","journal-title":"IEEE Trans. Geosc. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1054","DOI":"10.1109\/LGRS.2020.2991760","article-title":"Improving SAR-Based Coherent Change Detection Products by Using an Alternate Coherency Formalism","volume":"18","author":"Sabry","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","unstructured":"Hammer, H., Lorenz, F., Cadario, E., Kuny, S., and Thiele, A. (April, January 29). Enhancement of Coherence Images for Coherent Change Detection. Proceedings of the European Conference on Synthetic Aperture Radar (EUSAR 2021), Virtual."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hammer, H., Thiele, A., Lorenz, F., Cadario, E., Kuny, S., and Schulz, K. (2019, January 9\u201311). A Comparative Study of Coherence Estimators for Interferometric SAR Image Co-Registration and Coherent Change Detection. Proceedings of the SPIE, Image and Signal Processing for Remote Sensing XXV, Strasbourg, France.","DOI":"10.1117\/12.2533147"},{"key":"ref_19","unstructured":"Gatelli, G., Monti-Guarnieri, A., and Prati, C. (1996, January 10\u201313). Coherence Estimation of Interferometric SAR Images. Proceedings of the 1996 8th European Signal Processing Conference (EUSIPCO 1996), Trieste, Italy."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1109\/TGRS.2010.2076376","article-title":"NL-InSAR: Non-Local Interferogram Estimation","volume":"49","author":"Deledalle","year":"2011","journal-title":"IEEE Trans. Geosc. Remote Sens."},{"key":"ref_21","unstructured":"Mu, D., Lai, C., and Lin, Y. (2007, January 5\u20139). Modifying of the Coherence Estimator for Interferometric SAR. Proceedings of the 1st Asian and Pacific Conference on Synthetic Aperture Radar (APSAR2007), Huangshan, China."},{"key":"ref_22","unstructured":"Lopez-Mart\u00ednez, C. (2003). Multidimensional Speckle Noise, Modeling and Filtering Related to SAR Data. [Ph.D. Thesis, Universitat Polit\u00e8cnica de Catalunya]."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Banerjee, S., and Sinha Chaudhuri, S. (2018, January 15\u201316). A Review on various Speckle Filters used for despeckling SAR images. Proceedings of the Second International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC.2018.8487958"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"48","DOI":"10.9790\/0661-1901024854","article-title":"Analysis of Adaptive and Advanced Speckle Filters on SAR Data","volume":"19","author":"Misra","year":"2017","journal-title":"IOSR J. Comput. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cozzolino, D., Verdoliva, L., Scarpa, G., and Poggi, G. (2020). Nonlocal CNN SAR Image Despeckling. Remote Sens., 12.","DOI":"10.3390\/rs12061006"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yuan, Q., Li, J., Yang, Z., Ma, X., Shen, H., and Zhang, L. (2018). Learning a Dilated Residual Network for SAR Image Despeckling. Remote Sens., 10.","DOI":"10.3390\/rs10020196"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TPAMI.1980.4766994","article-title":"Digital Image Enhancement and Noise Filtering by Use of Local Statistics","volume":"PAMI-2","author":"Lee","year":"1980","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1080\/01431169308953999","article-title":"Structure Detection and Statistical Adaptive Speckle Filtering in SAR Images","volume":"14","author":"Lopes","year":"1993","journal-title":"Intern. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1109\/TGRS.2014.2352555","article-title":"NL-SAR: A Unified Non-Local Framework for Resolution-Preserving (Pol)(In)SAR Denoising","volume":"53","author":"Deledalle","year":"2015","journal-title":"IEEE Trans. Geosc. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(85)90125-2","article-title":"A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram","volume":"29","author":"Kapur","year":"1985","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cyber."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/34.659930","article-title":"An unbiased detector of curvilinear structures","volume":"20","author":"Steger","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. 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