{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T14:22:10Z","timestamp":1785853330465,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T00:00:00Z","timestamp":1612396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["237906"],"award-info":[{"award-number":["237906"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Robust and reliable classification of sea ice types in synthetic aperture radar (SAR) images is needed for various operational and environmental applications. Previous studies have investigated the class-dependent decrease in SAR backscatter intensity with incident angle (IA); others have shown the potential of textural information to improve automated image classification. In this work, we investigate the inclusion of Sentinel-1 (S1) texture features into a Bayesian classifier that accounts for linear per-class variation of its features with IA. We use the S1 extra-wide swath (EW) product in ground-range detected format at medium resolution (GRDM), and we compute seven grey level co-occurrence matrix (GLCM) texture features from the HH and the HV backscatter intensity in the linear and logarithmic domain. While GLCM texture features obtained in the linear domain vary significantly with IA, the features computed from the logarithmic intensity do not depend on IA or reveal only a weak, approximately linear dependency. They can therefore be directly included in the IA-sensitive classifier that assumes a linear variation. The different number of looks in the first sub-swath (EW1) of the product causes a distinct offset in texture at the sub-swath boundary between EW1 and the second sub-swath (EW2). This offset must be considered when using texture in classification; we demonstrate a manual correction for the example of GLCM contrast. Based on the Jeffries\u2013Matusita distance between class histograms, we perform a separability analysis for 57 different GLCM parameter settings. We select a suitable combination of features for the ice classes in our data set and classify several test images using a combination of intensity and texture features. We compare the results to a classifier using only intensity. Particular improvements are achieved for the generalized separation of ice and water, as well as the classification of young ice and multi-year ice.<\/jats:p>","DOI":"10.3390\/rs13040552","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T21:29:27Z","timestamp":1612474167000},"page":"552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Incident Angle Dependence of Sentinel-1 Texture Features for Sea Ice Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8038-8572","authenticated-orcid":false,"given":"Johannes","family":"Lohse","sequence":"first","affiliation":[{"name":"Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9345-6896","authenticated-orcid":false,"given":"Anthony P.","family":"Doulgeris","sequence":"additional","affiliation":[{"name":"Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Troms\u00f8, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5031-648X","authenticated-orcid":false,"given":"Wolfgang","family":"Dierking","sequence":"additional","affiliation":[{"name":"Department of Physics and Technology, UiT The Arctic University of Norway, 9019 Troms\u00f8, Norway"},{"name":"Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Bussestr. 24, 27570 Bremerhaven, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1080\/07038992.1998.10874689","article-title":"Use of RADARSAT data in the Canadian Ice Service","volume":"24","author":"Ramsay","year":"1998","journal-title":"Can. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1109\/TGRS.2009.2031806","article-title":"Mapping of Different Sea Ice Regimes Using Images from Sentinel-1 and ALOS Synthetic Aperture Radar","volume":"48","author":"Dierking","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zakhvatkina, N., Smirnov, V., and Bychkova, I. (2019). Satellite SAR Data-based Sea Ice Classification: An Overview. Geosciences, 9.","DOI":"10.3390\/geosciences9040152"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100","DOI":"10.5670\/oceanog.2013.33","article-title":"Sea Ice Monitoring by Synthetic Aperture Radar","volume":"26","author":"Dierking","year":"2013","journal-title":"Oceanography"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.5194\/tc-7-1693-2013","article-title":"Comparison of feature based segmentation of full polarimetric SAR satellite sea ice images with manually drawn ice charts","volume":"7","author":"Moen","year":"2013","journal-title":"Cryosphere"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.5194\/tc-14-1289-2020","article-title":"Accuracy and inter-analyst agreement of visually estimated sea ice concentrations in Canadian Ice Service ice charts using single-polarization RADARSAT-2","volume":"14","author":"Cheng","year":"2020","journal-title":"Cryosphere"},{"key":"ref_7","unstructured":"Dierking, W. (2020). Sea Ice and Icebergs. Maritime Surveillance with Synthetic Aperture Radar, Institution of Engineering and Technology."},{"key":"ref_8","unstructured":"Ulaby, F.T., Moore, R.K., and Fung, A.K. (1981). Microwave Remote Sensing: Active and Passive. Volume 1-Microwave Remote Sensing Fundamentals and Radiometry, Addison-Wesley."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1029\/GM068p0073","article-title":"SAR and scatterometer signatures of sea ice","volume":"68","author":"Onstott","year":"1992","journal-title":"Microw. Remote Sens. Sea Ice"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2593","DOI":"10.1109\/TGRS.2002.806991","article-title":"Incidence angle dependence of the statistical properties of C-band HH-polarization backscattering signatures of the Baltic Sea ice","volume":"40","author":"Manninen","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2015.06.005","article-title":"Sensitivity of C-band synthetic aperture radar polarimetric parameters to snow thickness over landfast smooth first-year sea ice","volume":"166","author":"Gill","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6170","DOI":"10.1109\/TGRS.2017.2721981","article-title":"Incidence Angle Dependence of First-Year Sea Ice Backscattering Coefficient in Sentinel-1 SAR Imagery Over the Kara Sea","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6686","DOI":"10.1109\/TGRS.2018.2841343","article-title":"Incidence Angle Dependence of HH-Polarized C- and L-Band Wintertime Backscatter Over Arctic Sea Ice","volume":"56","author":"Mahmud","year":"2018","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2587","DOI":"10.1109\/TGRS.2012.2212445","article-title":"Classification of Sea Ice Types in ENVISAT Synthetic Aperture Radar Images","volume":"51","author":"Zakhvatkina","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.5194\/tc-8-1639-2014","article-title":"A sea ice concentration estimation algorithm utilizing radiometer and SAR data","volume":"8","author":"Karvonen","year":"2014","journal-title":"Cryosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1109\/JSTARS.2014.2365215","article-title":"SVM-Based Sea Ice Classification Using Textural Features and Concentration From RADARSAT-2 Dual-Pol ScanSAR Data","volume":"8","author":"Liu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1109\/TGRS.2017.2655567","article-title":"Baltic Sea Ice Concentration Estimation Using SENTINEL-1 SAR and AMSR2 Microwave Radiometer Data","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"33","DOI":"10.5194\/tc-11-33-2017","article-title":"Operational algorithm for ice\u2013water classification on dual-polarized RADARSAT-2 images","volume":"11","author":"Zakhvatkina","year":"2017","journal-title":"Cryosphere"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/aog.2020.45","article-title":"Mapping Sea Ice Types from Sentinel-1 Considering the Surface-Type Dependent Effect of Incidence Angle","volume":"61","author":"Lohse","year":"2020","journal-title":"Ann. Glaciol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1109\/TGRS.1984.350602","article-title":"Textural Analysis And Real-Time Classification of Sea-Ice Types Using Digital SAR Data","volume":"GE-22","author":"Holmes","year":"1984","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_21","first-page":"385","article-title":"SAR Sea Ice Discrimination Using Texture Statistics: A Multivariate Approach","volume":"57","author":"Barber","year":"1991","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10625","DOI":"10.1029\/91JC00693","article-title":"Evaluation of second-order texture parameters for sea ice classification from radar images","volume":"96","author":"Shokr","year":"1991","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/36.752194","article-title":"Texture Analysis of SAR Sea Ice Imagery Using Gray Level Co-Occurance Matrices","volume":"37","author":"Soh","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/07055900.2001.9649675","article-title":"Comparison and fusion of co-occurrence, Gabor and MRF texture features for classification of SAR sea-ice imagery","volume":"39","author":"Clausi","year":"2001","journal-title":"Atmosphere-Ocean"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5589\/m02-004","article-title":"An analysis of co-occurrence texture statistics as a function of grey level quantization","volume":"28","author":"Clausi","year":"2002","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1109\/TGRS.2004.839589","article-title":"Unsupervised segmentation of synthetic aperture Radar sea ice imagery using a novel Markov random field model","volume":"43","author":"Deng","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1109\/JSTARS.2015.2436993","article-title":"A Neural Network-Based Classification for Sea Ice Types on X-Band SAR Images","volume":"8","author":"Ressel","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_29","unstructured":"Clausi, D.A., and Deng, H. (2003, January 27\u201328). Operational segmentation and classification of SAR sea ice imagery. Proceedings of the IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, 2003, Greenbelt, MD, USA."},{"key":"ref_30","unstructured":"Aulard-Macler, M. (2011). Sentinel-1 Product Definition S1-RS-MDA-52-7440, MacDonald, Dettwiler and Associates Ltd.. Technical Report."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Sen, R., Goswami, S., and Chakraborty, B. (2019). Jeffries-Matusita distance as a tool for feature selection. Proceedings of the 2019 International Conference on Data Science and Engineering (ICDSE), Patna, India, 26\u201328 September 2019, IEEE.","DOI":"10.1109\/ICDSE47409.2019.8971800"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lohse, J., Doulgeris, A.P., and Dierking, W. (2019). An Optimal Decision-Tree Design Strategy and Its Application to Sea Ice Classification from SAR Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11131574"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3256","DOI":"10.1109\/TGRS.2010.2043954","article-title":"C-band polarimetric backscattering signatures of newly formed sea ice during fall freeze-up","volume":"48","author":"Isleifson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9109","DOI":"10.1109\/TGRS.2019.2924868","article-title":"Detection of first-year and multi-year sea ice from dual-polarization SAR images under cold conditions","volume":"57","author":"Komarov","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"7233","DOI":"10.1109\/TGRS.2014.2310136","article-title":"Retrieval of Arctic sea ice parameters by satellite passive microwave sensors: A comparison of eleven sea ice concentration algorithms","volume":"52","author":"Ivanova","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"980","DOI":"10.1109\/TGRS.2013.2246171","article-title":"Ocean surface wind speed retrieval from C-band SAR images without wind direction input","volume":"52","author":"Komarov","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/552\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:20:11Z","timestamp":1760160011000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/4\/552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,4]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["rs13040552"],"URL":"https:\/\/doi.org\/10.3390\/rs13040552","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,4]]}}}