{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,9]],"date-time":"2024-08-09T00:15:06Z","timestamp":1723162506255},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Fund","doi-asserted-by":"publisher","award":["61901445"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Municipal Natural Science Foundation","award":["4192065"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The Wishart mixture model is an effective tool for characterizing the statistical distribution of polarimetric synthetic aperture radar (PolSAR) data. However, due to the difficulty in determining the equivalent number of looks, the Wishart mixture model has some problems in terms of practicality. In addition, the flexibility of the Wishart mixture model needs to be improved for complicated scenes. To improve the practicality and flexibility, a new mixture model named the relaxed Wishart mixture model (RWMM) is proposed. In RWMM, the equivalent number of looks is no longer considered a constant for the whole PolSAR image but a variable that varies between different clusters. Next, an innovative algorithm named RWMM-Markov random field (RWMM-MRFt) for supervised classification is proposed. A new selection criterion for adaptive neighborhood systems is proposed in the algorithm to improve the classification performance. The new criterion makes effective use of PolSAR scattering information to select the most suitable neighborhood for each center pixel in PolSAR images. Three datasets, including one simulated image and two real PolSAR images, are utilized in the experiment. The maximum likelihood classification results demonstrate the flexibility of the proposed RWMM for modeling PolSAR data. The proposed selection criterion shows superior performance than the span-based selection criterion. Among the mixture model-based MRF classification algorithms, the proposed RWMM-MRFt algorithm has the highest classification accuracy, and the corresponding classification maps have better anti-noise performance.<\/jats:p>","DOI":"10.3390\/rs14215506","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T07:36:44Z","timestamp":1667374604000},"page":"5506","source":"Crossref","is-referenced-by-count":2,"title":["An Innovative Supervised Classification Algorithm for PolSAR Image Based on Mixture Model and MRF"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5499-7470","authenticated-orcid":false,"given":"Mingliang","family":"Liu","sequence":"first","affiliation":[{"name":"Space Microwave Remote Sensing System Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yunkai","family":"Deng","sequence":"additional","affiliation":[{"name":"Space Microwave Remote Sensing System Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chuanzhao","family":"Han","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing Information, Beijing 100192, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5665-9478","authenticated-orcid":false,"given":"Wentao","family":"Hou","sequence":"additional","affiliation":[{"name":"Space Microwave Remote Sensing System Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0860-5613","authenticated-orcid":false,"given":"Yao","family":"Gao","sequence":"additional","affiliation":[{"name":"Space Microwave Remote Sensing System Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chunle","family":"Wang","sequence":"additional","affiliation":[{"name":"Space Microwave Remote Sensing System Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xiuqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Space Microwave Remote Sensing System Department, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,1]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"3426","DOI":"10.1109\/TGRS.2007.907192","article-title":"A new application for PolSAR imagery in the field of moving target indication\/ship detection","volume":"45","author":"Liu","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.3390\/rs4082314","article-title":"Polarimetric decomposition analysis of ALOS PALSAR observation data before and after a landslide event","volume":"4","author":"Yonezawa","year":"2012","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/TGRS.2012.2210050","article-title":"Tsunami damage investigation of built-up areas using multitemporal spaceborne full polarimetric SAR images","volume":"51","author":"Chen","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"649","article-title":"The NASA\/JPL three-frequency polarimetric AIRSAR system","volume":"Volume 1","author":"Carande","year":"1992","journal-title":"Proceedings of the IGARSS\u201992 International Geoscience and Remote Sensing Symposium"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"221","DOI":"10.5589\/m04-004","article-title":"An introduction to the RADARSAT-2 mission","volume":"30","author":"Morena","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sun, J., Yu, W., and Deng, Y. (2017). The SAR payload design and performance for the GF-3 mission. Sensors, 17.","DOI":"10.3390\/s17102419"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105542","DOI":"10.1016\/j.knosys.2020.105542","article-title":"Dense connection and depthwise separable convolution based CNN for polarimetric SAR image classification","volume":"194","author":"Shang","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shang, R., Wang, G., Okoth, M.A., and Jiao, L. (2019). Complex-valued convolutional autoencoder and spatial pixel-squares refinement for polarimetric SAR image classification. Remote Sens., 11.","DOI":"10.3390\/rs11050522"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lee, J.S., and Pottier, E. (2017). Polarimetric Radar Imaging: From Basics to Applications, CRC Press.","DOI":"10.1201\/9781420054989"},{"key":"ref_11","first-page":"171","article-title":"Identification of terrain cover using the optimum polarimetric classifier","volume":"2","author":"Kong","year":"1988","journal-title":"J. Electromagn. Waves Appl."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"5777","DOI":"10.1109\/TGRS.2017.2714169","article-title":"Copula-based joint statistical model for polarimetric features and its application in PolSAR image classification","volume":"55","author":"Dong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/36.581979","article-title":"Optimal speckle reduction for the product model in multilook polarimetric SAR imagery and the Wishart distribution","volume":"35","author":"Lopes","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1109\/IGARSS.1994.399685","article-title":"K-distribution for multi-look processed polarimetric SAR imagery","volume":"Volume 4","author":"Lee","year":"1994","journal-title":"Proceedings of the IGARSS\u201994-1994 IEEE International Geoscience and Remote Sensing Symposium"},{"key":"ref_16","first-page":"13","article-title":"The polarimetric G distribution for SAR data analysis","volume":"16","author":"Freitas","year":"2005","journal-title":"Environ. Off. J. Int. Environ. Soc."},{"key":"ref_17","unstructured":"Harant, O., Bombrun, L., Gay, M., Fallourd, R., Trouv\u00e9, E., and Tupin, F. (2009, January 26\u201330). Segmentation and classification of polarimetric SAR data based on the KummerU distribution. Proceedings of the POLinSAR 2009-4th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, Frascati, Italy."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.3390\/rs6053770","article-title":"Land cover classification for polarimetric SAR images based on mixture models","volume":"6","author":"Gao","year":"2014","journal-title":"Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TGRS.2004.842108","article-title":"Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering","volume":"43","author":"Kersten","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","unstructured":"Anfinsen, S.N., Eltoft, T., and Doulgeris, A.P. (2009, January 26\u201330). A relaxed Wishart model for polarimetric SAR data. Proceedings of the PolInSAR, Frascati, Italy."},{"key":"ref_21","unstructured":"Li, S.Z. (2009). Markov Random Field Modeling in Image Analysis, Springer Science & Business Media."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.1109\/83.413180","article-title":"Image classification using spectral and spatial information based on MRF models","volume":"4","author":"Yamazaki","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/LGRS.2013.2250905","article-title":"Hyperspectral image classification using Gaussian mixture models and Markov random fields","volume":"11","author":"Li","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1109\/LGRS.2008.2002263","article-title":"Region-based classification of polarimetric SAR images using Wishart MRF","volume":"5","author":"Wu","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/36.602527","article-title":"Synthetic aperture radar image segmentation by a detail preserving Markov random field approach","volume":"35","author":"Smits","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"513","DOI":"10.3390\/e11030513","article-title":"Scale-based gaussian coverings: Combining intra and inter mixture models in image segmentation","volume":"11","author":"Murtagh","year":"2009","journal-title":"Entropy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14397","DOI":"10.3390\/s121114397","article-title":"A coded aperture compressive imaging array and its visual detection and tracking algorithms for surveillance systems","volume":"12","author":"Chen","year":"2012","journal-title":"Sensors"},{"key":"ref_28","unstructured":"Oliver, C., and Quegan, S. (2004). Understanding Synthetic Aperture Radar Images, SciTech Publishing."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3795","DOI":"10.1109\/TGRS.2009.2019269","article-title":"Estimation of the equivalent number of looks in polarimetric synthetic aperture radar imagery","volume":"47","author":"Anfinsen","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"McLachlan, G.J., and Krishnan, T. (2007). The EM Algorithm and Extensions, John Wiley & Sons.","DOI":"10.1002\/9780470191613"},{"key":"ref_31","unstructured":"Kulis, B., Sustik, M.A., and Dhillon, I.S. (2009). Low-Rank Kernel Learning with Bregman Matrix Divergences. J. Mach. Learn. Res., 10."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/TPAMI.1984.4767596","article-title":"Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images","volume":"PAMI-6","author":"Geman","year":"1984","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1364\/JOSAA.8.001499","article-title":"Segmentation of synthetic-aperture-radar complex data","volume":"8","author":"Rignot","year":"1991","journal-title":"JOSA A"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TGRS.2007.907601","article-title":"An Unsupervised Segmentation With an Adaptive Number of Clusters Using the SPAN\/H\/\u03b1\/A Space and the Complex Wishart Clustering for Fully Polarimetric SAR Data Analysis","volume":"45","author":"Cao","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3953","DOI":"10.1109\/TGRS.2016.2532320","article-title":"Polarimetric SAR change detection with the complex Hotelling\u2013Lawley trace statistic","volume":"54","author":"Akbari","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TGRS.2010.2103945","article-title":"Application of the matrix-variate Mellin transform to analysis of polarimetric radar images","volume":"49","author":"Anfinsen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3040","DOI":"10.1109\/TGRS.2018.2879984","article-title":"Polarimetric convolutional network for PolSAR image classification","volume":"57","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1109\/TGRS.2008.2008309","article-title":"Characterizing L-band scattering of paddy rice in southeast China with radiative transfer model and multitemporal ALOS\/PALSAR imagery","volume":"47","author":"Wang","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1109\/TGRS.2011.2140120","article-title":"Automated non-Gaussian clustering of polarimetric synthetic aperture radar images","volume":"49","author":"Doulgeris","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5506\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T21:30:56Z","timestamp":1723152656000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,1]]},"references-count":39,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215506"],"URL":"https:\/\/doi.org\/10.3390\/rs14215506","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,11,1]]}}}