{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:57:18Z","timestamp":1760147838377,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771467"],"award-info":[{"award-number":["41771467"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time\/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle noise of SAR images will affect the accuracy of building extraction. This paper proposes a novel building extraction approach from PolSAR images with statistical texture and polarization features by using a convolutional neural network and superpixel. A feature space that is sensitive to building, including G0 statistical texture and PualiRGB features, is constructed and used as CNN input. Considering that the building boundary of the CNN classification result is inaccurate due to speckle noise, the simple linear iterative cluster (SLIC) superpixel is utilized to constrain the building extraction result. Finally, the effectiveness of the proposed method has been verified by experimenting with PolSAR images from three different sensors, including ESAR, GF-3, and RADARSAT-2. Experiment results show that compared with the other five PolSAR building extraction methods including threshold, SVM, RVCNN, and PFDCNN, our method without superpixel constraint, the F1-score of this method is the highest, reaching 84.22%, 91.24%, and 87.49%, respectively. The false alarm rate of this method is at least 10% lower and the F1 index is at least 6% higher when the building extraction accuracy is comparable. Further, the discussion and method parameter analysis results show that increasing the use of G0 statistical texture parameters can improve building extraction accuracy and reduce false alarms, and the introduction of superpixel constraints can further reduce false alarms.<\/jats:p>","DOI":"10.3390\/rs15051451","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1451","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PolSAR Image Building Extraction with G0 Statistical Texture Using Convolutional Neural Network and Superpixel"],"prefix":"10.3390","volume":"15","author":[{"given":"Mei","family":"Li","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qikai","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0045-9642","authenticated-orcid":false,"given":"Xiuguo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7250-8781","authenticated-orcid":false,"given":"Qihao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1109\/LGRS.2011.2178392","article-title":"A new approach to collapsed building extraction using RADARSAT-2 polarimetric SAR imagery","volume":"9","author":"Li","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.isprsjprs.2016.03.009","article-title":"Unsupervised polarimetric SAR urban area classifification based on model-based decomposition with cross scattering","volume":"116","author":"Xiang","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2012.700133","article-title":"Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach","volume":"34","author":"Niu","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1109\/LGRS.2015.2487450","article-title":"Model-Based Decomposition With Cross Scattering for Polarimetric SAR Urban Areas","volume":"12","author":"Xiang","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1080\/01431169408954244","article-title":"Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution","volume":"15","author":"Lee","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4058","DOI":"10.1109\/JSTARS.2013.2281594","article-title":"Improved building extraction with integrated decomposition of time-frequency and entropy-alpha using polarimetric SAR data","volume":"7","author":"Deng","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, Q., Chen, Q., Yang, S., and Liu, X. (2016). Superpixel-Based Classification Using K Distribution and Spatial Context for Polarimetric SAR Images. Remote Sens., 8.","DOI":"10.3390\/rs8080619"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/JSTARS.2019.2954292","article-title":"A Novel Statistical Texture Feature for SAR Building Damage Assessment in Different Polarization Modes","volume":"13","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","first-page":"475","article-title":"A Multi-scale SVM-CRF Model for Buildings Extraction from Polarimetric SAR Images","volume":"32","author":"Ping","year":"2017","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/2150704X.2015.1101179","article-title":"Fusion of polarimetric and texture information for urban building extraction from fully polarimetric SAR imagery","volume":"7","author":"Zhai","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1109\/JSTARS.2017.2787591","article-title":"Eigenvalue-Based Urban Area Extraction Using Polarimetric SAR Data","volume":"11","author":"Quan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0924-2716(03)00017-0","article-title":"Classification of polarimetric SAR images of suburban areas using joint annealed segmentation and \u201cH\/A\/\u03b1\u201d polarimetric decomposition","volume":"58","author":"Pellizzeri","year":"2003","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yan, L., Zhang, J., Huang, G., and Zhao, Z. (2011, January 9\u201311). Building Footprints Extraction from PolSAR Image Using Multi-Features and Edge Information. Proceedings of the 2011 International Symposium on Image and Data Fusion, Tengchong, China.","DOI":"10.1109\/ISIDF.2011.6024275"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.3390\/rs70201380","article-title":"Use of Sub-Aperture Decomposition for Supervised PolSAR Classification in Urban Area","volume":"7","author":"Deng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.rse.2017.03.030","article-title":"Slum mapping in polarimetric SAR data using spatial features","volume":"194","author":"Wurm","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/JSTARS.2017.2752282","article-title":"A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data","volume":"11","author":"De","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9378","DOI":"10.1109\/TGRS.2019.2926434","article-title":"An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification","volume":"57","author":"Bi","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/LGRS.2018.2799877","article-title":"PolSAR Image Classification Using Polarimetric-Feature-Driven Deep Convolutional Neural Network","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.image.2017.04.007","article-title":"Superpixel segmentation: A benchmark","volume":"56","author":"Wang","year":"2017","journal-title":"Signal Process. Image Commun."},{"key":"ref_23","first-page":"1","article-title":"Superpixel-Based Cropland Classification of SAR Image With Statistical Texture and Polarization Features","volume":"19","author":"Chen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, X., Wang, W., and Yang, E. (2013, January 22\u201323). Urban construction area extraction using circular polarimetric correlation coefficient. Proceedings of the 2013 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2013.6729721"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/TGRS.2019.2933483","article-title":"Superpixel-Driven Optimized Wishart Network for Fast PolSAR Image Classification Using Global k-Means Algorithm","volume":"58","author":"Gadhiya","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xia, J., Tan, X., Zhou, X., and Wang, T. (2019). PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features. Remote Sens., 11.","DOI":"10.3390\/rs11151831"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Krogager, E., Boerner, W.M., and Madsen, S. (1997, January 28\u201329). Feature-motivated Sinclair matrix (sphere\/diplane\/helix) decomposition and its application to target sorting for land feature classification. Proceedings of the SPIE Conference on Wideband Interferometric Sensing and Imaging Polarimetry, San Diego, CA, USA.","DOI":"10.1117\/12.300620"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1002\/env.658","article-title":"The polarimetric G distribution for SAR data analysis","volume":"16","author":"Freitas","year":"2005","journal-title":"Environmetrics"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1080\/00401706.1966.10490365","article-title":"Probability, Random Variables, and Stochastic Processesby Anthanasios Papoulis","volume":"8","author":"Miller","year":"1966","journal-title":"Technometrics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2063","DOI":"10.1109\/TGRS.2004.835302","article-title":"Segmentation of textured polarimetric SAR scenes by likelihood approximation","volume":"42","author":"Beaulieu","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3502","DOI":"10.1109\/TGRS.2013.2273128","article-title":"On fractional moments of multilook polarimetric whitening filter for polarimetric SAR data","volume":"52","author":"Khan","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1109\/TGRS.2008.923025","article-title":"Classification with a Non-Gaussian Model for PolSAR Data","volume":"46","author":"Doulgeris","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MCI.2010.938364","article-title":"Deep Machine Learning\u2014A New Frontier in Artificial Intelligence Research [Research Frontier]","volume":"5","author":"Arel","year":"2010","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Strigl, D., Kofler, K., and Podlipnig, S. (2010, January 17\u201319). Performance and scalability of gpu-based convolutional neural networks. Proceedings of the 2010 18th Euromicro Conference on Parallel, Pisa, Italy.","DOI":"10.1109\/PDP.2010.43"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mnih, V., and Hinton, G.E. (2010, January 5\u201311). Learning to detect roads in high-resolution aerial images. Proceedings of the 11th European Conference on Computer Vision, Heraklion, Greece.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning multiscale and deep representations for classifying remotely sensed imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000006","article-title":"Learning Deep Architectures for AI","volume":"2","author":"Bengio","year":"2009","journal-title":"Found. Trends Mach. Learn."},{"key":"ref_40","first-page":"352","article-title":"A kernel functions analysis for support vector machines for land cover classification","volume":"11","author":"Kavzoglu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_41","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS-J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1451\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:48:02Z","timestamp":1760122082000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1451"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051451"],"URL":"https:\/\/doi.org\/10.3390\/rs15051451","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,3,4]]}}}