{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T18:12:25Z","timestamp":1766599945058,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T00:00:00Z","timestamp":1648425600000},"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>The existence of multiplicative noise in synthetic aperture radar (SAR) images makes SAR segmentation by fuzzy c-means (FCM) a challenging task. To cope with speckle noise, we first propose an unsupervised FCM with embedding log-transformed Bayesian non-local spatial information (LBNL_FCM). This non-local information is measured by a modified Bayesian similarity metric which is derived by applying the log-transformed SAR distribution to Bayesian theory. After, we construct the similarity metric of patches as the continued product of corresponding pixel similarity measured by generalized likelihood ratio (GLR) to avoid the undesirable characteristics of log-transformed Bayesian similarity metric. An alternative unsupervised FCM framework named GLR_FCM is then proposed. In both frameworks, an adaptive factor based on the local intensity entropy is employed to balance the original and non-local spatial information. Additionally, the membership degree smoothing and the majority voting idea are integrated as supplementary local information to optimize segmentation. Concerning experiments on simulated SAR images, both frameworks can achieve segmentation accuracy of over 97%. On real SAR images, both unsupervised FCM segmentation frameworks work well on SAR homogeneous segmentation in terms of region consistency and edge preservation.<\/jats:p>","DOI":"10.3390\/rs14071621","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:45:51Z","timestamp":1648590351000},"page":"1621","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["SAR Image Segmentation by Efficient Fuzzy C-Means Framework with Adaptive Generalized Likelihood Ratio Nonlocal Spatial Information Embedded"],"prefix":"10.3390","volume":"14","author":[{"given":"Jingxing","family":"Zhu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6494-3639","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Hongjian","family":"You","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1049\/iet-cvi.2014.0295","article-title":"Unsupervised feature learning based on sparse coding and spectral clustering for segmentation of synthetic aperture radar images","volume":"9","author":"Rahmani","year":"2015","journal-title":"IET Comput. Vis."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jiao, S., Li, X., and Lu, X. (2006, January 16\u201320). An Improved Ostu Method for Image Segmentation. Proceedings of the 2006 8th international Conference on Signal Processing, Guilin, China.","DOI":"10.1109\/ICOSP.2006.345705"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2126","DOI":"10.1109\/TPAMI.2008.15","article-title":"IRGS: Image Segmentation Using Edge Penalties and Region Growing","volume":"30","author":"Yu","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1016\/j.dsp.2009.10.014","article-title":"SAR imagery segmentation by statistical region growing and hierarchical merging","volume":"20","author":"Carvalho","year":"2010","journal-title":"Digit. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9319","DOI":"10.1109\/TGRS.2020.3041281","article-title":"Fast Pixel-Superpixel Region Merging for SAR Image Segmentation","volume":"59","author":"Xiang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1109\/TGRS.2012.2203604","article-title":"Context-Based Hierarchical Unequal Merging for SAR Image Segmentation","volume":"51","author":"Yu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/TGRS.2017.2745507","article-title":"Optimal segmentation of high-resolution remote sensing image by combining superpixels with the minimum spanning tree","volume":"56","author":"Wang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"1","article-title":"Fast Task-Specific Region Merging for SAR Image Segmentation","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"1","article-title":"Fast Multiscale Superpixel Segmentation for SAR Imagery","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2126","DOI":"10.1109\/TGRS.2008.918647","article-title":"Spectral Clustering Ensemble Applied to SAR Image Segmentation","volume":"46","author":"Zhang","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mukhopadhaya, S., Kumar, A., and Stein, A. (2018). FCM Approach of Similarity and Dissimilarity Measures with \u03b1-Cut for Handling Mixed Pixels. Remote Sens., 10.","DOI":"10.20944\/preprints201809.0146.v1"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xu, Y., Chen, R., Li, Y., Zhang, P., Yang, J., Zhao, X., Liu, M., and Wu, D. (2019). Multispectral image segmentation based on a fuzzy clustering algorithm combined with Tsallis entropy and a gaussian mixture model. Remote Sens., 11.","DOI":"10.3390\/rs11232772"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Madhu, A., Kumar, A., and Jia, P. (2021). Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13204163"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1049\/iet-rsn:20060128","article-title":"Integration of synthetic aperture radar image segmentation method using Markov random field on region adjacency graph","volume":"1","author":"Xia","year":"2007","journal-title":"IET Radar Sonar Navig."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/LGRS.2008.2001768","article-title":"SAR Image Segmentation Based on Level Set With Stationary Global Minimum","volume":"5","author":"Shuai","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bao, L., Lv, X., and Yao, J. (2021). Water extraction in SAR Images using features analysis and dual-threshold graph cut model. Remote Sens., 13.","DOI":"10.3390\/rs13173465"},{"key":"ref_17","first-page":"5517916","article-title":"Dimensionality reduction and classification of hyperspectral image via multi-structure unified discriminative embedding","volume":"60","author":"Luo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, F., Gao, F., Sun, J., Zhou, H., and Hussain, A. (2019). Weakly supervised segmentation of SAR imagery using superpixel and hierarchically adversarial CRF. Remote Sens., 11.","DOI":"10.3390\/rs11050512"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, C., Pei, J., Wang, Z., Huang, Y., Wu, J., Yang, H., and Yang, J. (2020). When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation. Remote Sens., 12.","DOI":"10.3390\/rs12233863"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Colin, A., Fablet, R., Tandeo, P., Husson, R., Peureux, C., Long\u00e9p\u00e9, N., and Mouche, A. (2022). Semantic Segmentation of Metoceanic Processes Using SAR Observations and Deep Learning. Remote Sens., 14.","DOI":"10.3390\/rs14040851"},{"key":"ref_21","first-page":"1","article-title":"A Refined Pyramid Scene Parsing Network for Polarimetric SAR Image Semantic Segmentation in Agricultural Areas","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","article-title":"FCM: The fuzzy c-means clustering algorithm","volume":"10","author":"Bezdek","year":"1984","journal-title":"Comput. Geosci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/42.996338","article-title":"A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data","volume":"21","author":"Ahmed","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TSMCB.2004.831165","article-title":"Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure","volume":"34","author":"Chen","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part Cybern."},{"key":"ref_25","unstructured":"Szilagyi, L., Benyo, Z., Szil\u00e1gyi, S.M., and Adam, H. (2003, January 17\u201321). MR brain image segmentation using an enhanced fuzzy c-means algorithm. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439), Cancun, Mexico."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.patcog.2006.07.011","article-title":"Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation","volume":"40","author":"Cai","year":"2007","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TIP.2010.2040763","article-title":"A robust fuzzy local information C-means clustering algorithm","volume":"19","author":"Krinidis","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","unstructured":"Buades, A., Coll, B., and Morel, J.M. (2005, January 20\u201325). A non-local algorithm for image denoising. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1016\/j.compmedimag.2008.08.004","article-title":"A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints","volume":"32","author":"Wang","year":"2008","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TSMCB.2008.2004818","article-title":"Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions","volume":"39","author":"Zhu","year":"2009","journal-title":"IEEE TRansactions Syst. Man Cybern. Part B Cybern."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s11704-010-0393-8","article-title":"Fuzzy c-means clustering with non local spatial information for noisy image segmentation","volume":"5","author":"Zhao","year":"2011","journal-title":"Front. Comput. Sci. China"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1016\/j.sigpro.2010.10.001","article-title":"A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation","volume":"91","author":"Zhao","year":"2011","journal-title":"Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.sigpro.2012.08.024","article-title":"Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation","volume":"93","author":"Feng","year":"2013","journal-title":"Signal Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1109\/JSTARS.2014.2308531","article-title":"A robust nonlocal fuzzy clustering algorithm with between-cluster separation measure for SAR image segmentation","volume":"7","author":"Ji","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/JSTARS.2018.2792841","article-title":"A robust fuzzy c-means algorithm based on Bayesian nonlocal spatial information for SAR image segmentation","volume":"11","author":"Wan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kervrann, C., Boulanger, J., and Coup\u00e9, P. (2007). Bayesian non-local means filter, image redundancy and adaptive dictionaries for noise removal. International Conference on Scale Space and Variational Methods in Computer Vision, Springer.","DOI":"10.1007\/978-3-540-72823-8_45"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s11263-012-0519-6","article-title":"How to compare noisy patches? Patch similarity beyond Gaussian noise","volume":"99","author":"Deledalle","year":"2012","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Springer.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TGRS.2002.1000333","article-title":"Statistical properties of logarithmically transformed speckle","volume":"40","author":"Xie","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1364\/JOSA.66.001145","article-title":"Some fundamental properties of speckle","volume":"66","author":"Goodman","year":"1976","journal-title":"JOSA"},{"key":"ref_41","unstructured":"Oliver, C., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, SciTech Publishing."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Shang, R., Lin, J., Jiao, L., and Li, Y. (2020). SAR Image Segmentation Using Region Smoothing and Label Correction. Remote Sens., 12.","DOI":"10.3390\/rs12050803"},{"key":"ref_43","first-page":"2719617","article-title":"A validity index for fuzzy clustering based on bipartite modularity","volume":"2019","author":"Liu","year":"2019","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/BF02339490","article-title":"Numerical taxonomy with fuzzy sets","volume":"1","author":"Bezdek","year":"1974","journal-title":"J. Math. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C. (1973). Cluster Validity with Fuzzy Sets, Taylor & Francis.","DOI":"10.1080\/01969727308546047"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/0167-8655(96)00026-8","article-title":"Validating fuzzy partitions obtained through c-shells clustering","volume":"17","author":"Dave","year":"1996","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","unstructured":"Fukuyama, Y. (1989, January 3). A new method of choosing the number of clusters for the fuzzy c-mean method. Proceedings of the 5th Fuzzy Systems Symposium, Kobe, Japan."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3575","DOI":"10.1007\/s13042-019-00945-2","article-title":"Meticulous fuzzy convolution C means for optimized big data analytics: Adaptation towards deep learning","volume":"10","author":"Balakrishnan","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_49","unstructured":"Wang, Y., Han, M., and Wu, Y. (2020, January 8\u201311). Semi-supervised Fault Diagnosis Model Based on Improved Fuzzy C-means Clustering and Convolutional Neural Network. Proceedings of the IOP Conference Series: Materials Science and Engineering. IOP Publishing, Shaanxi, China."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1621\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:45:05Z","timestamp":1760136305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1621"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,28]]},"references-count":49,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071621"],"URL":"https:\/\/doi.org\/10.3390\/rs14071621","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,3,28]]}}}