{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T20:11:56Z","timestamp":1760472716792,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T00:00:00Z","timestamp":1559606400000},"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>Accurate land use\/land cover classification of synthetic aperture radar (SAR) images plays an important role in environmental, economic, and nature related research areas and applications. When fully polarimetric SAR data is not available, single- or dual-polarization SAR data can also be used whilst posing certain difficulties. For instance, traditional Machine Learning (ML) methods generally focus on finding more discriminative features to overcome the lack of information due to single- or dual-polarimetry. Beside conventional ML approaches, studies proposing deep convolutional neural networks (CNNs) come with limitations and drawbacks such as requirements of massive amounts of data for training and special hardware for implementing complex deep networks. In this study, we propose a systematic approach based on sliding-window classification with compact and adaptive CNNs that can overcome such drawbacks whilst achieving state-of-the-art performance levels for land use\/land cover classification. The proposed approach voids the need for feature extraction and selection processes entirely, and perform classification directly over SAR intensity data. Furthermore, unlike deep CNNs, the proposed approach requires neither a dedicated hardware nor a large amount of data with ground-truth labels. The proposed systematic approach is designed to achieve maximum classification accuracy on single and dual-polarized intensity data with minimum human interaction. Moreover, due to its compact configuration, the proposed approach can process such small patches which is not possible with deep learning solutions. This ability significantly improves the details in segmentation masks. An extensive set of experiments over two benchmark SAR datasets confirms the superior classification performance and efficient computational complexity of the proposed approach compared to the competing methods.<\/jats:p>","DOI":"10.3390\/rs11111340","type":"journal-article","created":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T09:37:58Z","timestamp":1559727478000},"page":"1340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0937-5194","authenticated-orcid":false,"given":"Mete","family":"Ahishali","sequence":"first","affiliation":[{"name":"Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, FI-33720 Tampere, Finland"}]},{"given":"Serkan","family":"Kiranyaz","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Qatar University, Doha QA-2713, Qatar"}]},{"given":"Turker","family":"Ince","sequence":"additional","affiliation":[{"name":"Electrical and Electronics Engineering Department, Izmir University of Economics, Izmir TR-35330, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9788-2323","authenticated-orcid":false,"given":"Moncef","family":"Gabbouj","sequence":"additional","affiliation":[{"name":"Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, FI-33720 Tampere, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Endo, Y., Adriano, B., Mas, E., and Koshimura, S. (2018). New Insights into Multiclass Damage Classification of Tsunami-Induced Building Damage from SAR Images. Remote Sens., 10.","DOI":"10.3390\/rs10122059"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sun, T., Zhang, G., Perrie, W., Zhang, B., Guan, C., Khurshid, S., Warner, K., and Sun, J. (2018). Ocean Wind Retrieval Models for RADARSAT Constellation Mission Compact Polarimetry SAR. Remote Sens., 10.","DOI":"10.3390\/rs10121938"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Frison, P.-L., Fruneau, B., Kmiha, S., Soudani, K., Dufr\u00eane, E., Le Toan, T., Koleck, T., Villard, L., Mougin, E., and Rudant, J.-P. (2018). Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology. Remote Sens., 10.","DOI":"10.3390\/rs10122049"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Dabrowska-Zielinska, K., Musial, J., Malinska, A., Budzynska, M., Gurdak, R., Kiryla, W., Bartold, M., and Grzybowski, P. (2018). Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10121979"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.rse.2016.01.027","article-title":"Soil moisture retrieval over irrigated grassland using X-band SAR data","volume":"176","author":"Baghdadi","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"716","DOI":"10.3390\/rs5020716","article-title":"Recent trend and advance of synthetic aperture radar with selected topics","volume":"5","author":"Ouchi","year":"2013","journal-title":"Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2017.07.038","article-title":"The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas","volume":"200","author":"Santi","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jonsson, P. (2004). Vegetation as an urban climate control in the subtropical city of Gaborone, Botswana. Int. J. Climatol.","DOI":"10.1002\/joc.1064"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.rse.2005.11.016","article-title":"Remote sensing image-based analysis of the relationship between urban heat island and land use\/cover changes","volume":"104","author":"Chen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"911","DOI":"10.14358\/PERS.72.8.911","article-title":"Socioeconomic-Vegetation Relationships in Urban, Residential Land","volume":"11","author":"Mennis","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.1109\/TGRS.2011.2164085","article-title":"Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty","volume":"50","author":"Yu","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Amelard, R., Wong, A., and Clausi, D.A. (2013, January 21\u201326). Unsupervised classification of agricultural land cover using polarimetric synthetic aperture radar via a sparse texture dictionary model. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723806"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1109\/TGRS.2013.2258675","article-title":"Integrating color features in polarimetric SAR image classification","volume":"52","author":"Uhlmann","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TSMCB.2012.2187891","article-title":"Collective Network of Binary Classifier Framework for Polarimetric SAR Image Classification: An Evolutionary Approach","volume":"42","author":"Kiranyaz","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ince, T., Ahishali, M., and Kiranyaz, S. (2017, January 22\u201325). Comparison of polarimetric SAR features for terrain classification using incremental training. Proceedings of the Progress In Electromagnetics Research Symposium, St. Petersburg, Russia.","DOI":"10.1109\/PIERS.2017.8262319"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.isprsjprs.2014.01.005","article-title":"Classification of dual- and single polarized SAR images by incorporating visual features","volume":"90","author":"Uhlmann","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1109\/LGRS.2017.2702062","article-title":"A median regularized level set for hierarchical segmentation of SAR images","volume":"14","author":"Braga","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4565","DOI":"10.1109\/JSTARS.2017.2716620","article-title":"Level Set Segmentation Algorithm for High-Resolution Polarimetric SAR Images Based on a Heterogeneous Clutter Model","volume":"10","author":"Jin","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lang, F., Yang, J., Yan, S., and Qin, F. (2018). Superpixel Segmentation of Polarimetric Synthetic Aperture Radar (SAR) Images Based on Generalized Mean Shift. Remote Sens., 10.","DOI":"10.3390\/rs10101592"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TPAMI.2009.71","article-title":"Watershed cuts: Thinnings, shortest path forests, and topological watersheds","volume":"32","author":"Cousty","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.eswa.2017.04.018","article-title":"River channel segmentation in polarimetric SAR images: Watershed transform combined with average contrast maximisation","volume":"82","author":"Ciecholewski","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.3390\/rs6064801","article-title":"Semi-supervised learning for ill-posed polarimetric SAR classification","volume":"6","author":"Uhlmann","year":"2014","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Uhlmann, S., and Kiranyaz, S. (2013, January 21\u201326). Evaluation of classifiers for polarimetric SAR classification. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6721272"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Uhlmann, S., Kiranyaz, S., and Gabbouj, M. (2013, January 21\u201326). Polarimetric SAR classification using visual color features extracted over pseudo color images. Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723201"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Uhlmann, S., Kiranyaz, S., Gabbouj, M., and Ince, T. (2011, January 11\u201314). Incremental evolution of collective network of binary classifier for polarimetric SAR image classification. Proceedings of the International Conference on Image Processing, ICIP, Brussels, Belguim.","DOI":"10.1109\/INNOVATIONS.2011.5893823"},{"key":"ref_29","unstructured":"Uhlmann, S., Kiranyaz, S., Gabbouj, M., and Ince, T. (2011, January 11\u201315). Collective Network of Binary Classifier Framework for Polarimetric SAR Images. Proceedings of the IEEE Workshop on Evolving and Adaptive Intelligent Systems(EAIS), Paris, France."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Uhlmann, S., Kiranyaz, S., Ince, T., and Gabbouj, M. (2011, January 11\u201313). Polarimetric SAR Images Classification using Collective Network of Binary Classifiers. Proceedings of the Joint Urban Remote Sensing Event, JURSE 2011, Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764765"},{"key":"ref_31","unstructured":"Uhlmann, S., Kiranyaz, S., Ince, T., and Gabbouj, M. (September, January 29). SAR imagery classification in extended feature space by Collective Network of Binary Classifiers. Proceedings of the European Signal Processing Conference, Barcelona, Spain."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Uhlmann, S., Kiranyaz, S., Ince, T., and Gabbouj, M. (2011, January 7\u201310). Dynamic and data-driven classification for polarimetric SAR images. Proceedings of the SPIE\u2014The International Society for Optical Engineering, San Diego, CA, USA.","DOI":"10.1117\/12.897912"},{"key":"ref_33","first-page":"1","article-title":"Polarimetric SAR image classification using multifeatures combination and extremely randomized clustering forests","volume":"2010","author":"Yang","year":"2010","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_34","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems 25, Lake Tahoe, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., and Vanhoucke, V. (2017, January 4\u20139). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gao, F., Huang, T., Wang, J., Sun, J., Hussain, A., and Yang, E. (2017). Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification. Appl. Sci., 7.","DOI":"10.3390\/app7050447"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet Large Scale Visual Recognition Challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_42","unstructured":"Lee, J.-S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2003.819883","article-title":"Unsupervised terrain classification preserving polarimetric scattering characteristics","volume":"42","author":"Lee","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Hoekman, D.H. (2003). A New Polarimetric Classification Approach Evaluated for Agricultural Crops, European Space Agency, (Special Publication) ESA SP.","DOI":"10.1109\/TGRS.2003.817795"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1109\/36.964970","article-title":"Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR","volume":"39","author":"Lee","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3652","DOI":"10.1109\/TGRS.2010.2048115","article-title":"Polarimetric SAR Data in Land Cover Mapping in Boreal Zone","volume":"48","author":"Lonnqvist","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1109\/JSTARS.2012.2192915","article-title":"Classification accuracy of multi-frequency and multi-polarization SAR images for various land covers","volume":"5","author":"Turkar","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TGRS.2011.2172994","article-title":"Crop classification by multitemporal C- and L-band single- and dual-polarization and fully polarimetric SAR","volume":"50","author":"Skriver","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0031-3203(99)00032-1","article-title":"Rotation-invariant texture classification using feature distributions","volume":"33","author":"Ojala","year":"2000","journal-title":"Pattern Recognit."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/76.927424","article-title":"Color and texture descriptors","volume":"11","author":"Manjunath","year":"2001","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0923-5965(00)00016-3","article-title":"A texture descriptor for browsing and similarity retrieval","volume":"16","author":"Manjunath","year":"2000","journal-title":"J. Signal Process. Image Commun."},{"key":"ref_52","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_53","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/BF00130487","article-title":"Color indexing","volume":"7","author":"Swain","year":"1991","journal-title":"Int. J. Comput. Vis."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhang, C., and Li, S. (2006, January 16\u201319). A perceptive uniform pseudo-color coding method of SAR images. Proceedings of the CIE International Conference of Radar Proceedings, Shanghai, China.","DOI":"10.1109\/ICR.2006.343253"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/ptj\/85.3.257","article-title":"The kappa statistic in reliability studies: Use, interpretation, and sample size requirements","volume":"85","author":"Sim","year":"2005","journal-title":"Phys. Ther."},{"key":"ref_56","unstructured":"(2012, September 09). Corine Land Cover. Available online: http:\/\/sia.eionet.europa.eu\/CLC2006\/."},{"key":"ref_57","unstructured":"Chollet Fran\u00e7ois Keras: The Python Deep Learning library, keras.io."},{"key":"ref_58","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv, 2016."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.procs.2014.09.078","article-title":"Complexity analysis of multilayer perceptron neural network embedded into a wireless sensor network","volume":"36","author":"Serpen","year":"2014","journal-title":"Procedia Comput. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. (2018, January 18\u201322). V Learning Transferable Architectures for Scalable Image Recognition. Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_62","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1340\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:56:01Z","timestamp":1760187361000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1340"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,4]]},"references-count":62,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111340"],"URL":"https:\/\/doi.org\/10.3390\/rs11111340","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,6,4]]}}}