{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:00:19Z","timestamp":1760241619309,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T00:00:00Z","timestamp":1528156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science Foundation of China","award":["61703332","61773314"],"award-info":[{"award-number":["61703332","61773314"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the increase of resolution, effective characterization of synthetic aperture radar (SAR) image becomes one of the most critical problems in many earth observation applications. Inspired by deep learning and probability mixture models, a generalized Gamma deep belief network (g  \u0393-DBN) is proposed for SAR image statistical modeling and land-cover classification in this work. Specifically, a generalized Gamma-Bernoulli restricted Boltzmann machine (g\u0393B-RBM) is proposed to capture high-order statistical characterizes from SAR images after introducing the generalized Gamma distribution. After stacking the g  \u0393  B-RBM and several standard binary RBMs in a hierarchical manner, a g\u0393-DBN is constructed to learn high-level representation of different SAR land-covers. Finally, a discriminative neural network is constructed by adding an additional predict layer for different land-covers over the constructed deep structure. Performance of the proposed approach is evaluated via several experiments on some high-resolution SAR image patch sets and two large-scale scenes which are captured by ALOS PALSAR-2 and COSMO-SkyMed satellites respectively.<\/jats:p>","DOI":"10.3390\/rs10060878","type":"journal-article","created":{"date-parts":[[2018,6,5]],"date-time":"2018-06-05T11:04:46Z","timestamp":1528196686000},"page":"878","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["The Generalized Gamma-DBN for High-Resolution SAR Image Classification"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2475-7177","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhao","sequence":"first","affiliation":[{"name":"The School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[{"name":"Xi\u2019an Electronic Engineering Research Institute, Xi\u2019an 710100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Jia","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.1080\/01431161.2017.1407046","article-title":"Sensitivity Study of Radarsat-2 Polarimetric SAR to Crop Height and Fractional Vegetation Cover of Corn and Wheat","volume":"39","author":"Liao","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1080\/01431161.2017.1420938","article-title":"SAR-based Detection of Flooded Vegetation\u2014A Review of Characteristics and Approaches","volume":"39","author":"Tsyganskaya","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1109\/TGRS.2017.2769078","article-title":"Automatic Detection and Positioning of Ground Control Points Using TerraSAR-X Multiaspect Acquisitions","volume":"56","author":"Montazeri","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2050","DOI":"10.1109\/TGRS.2015.2494866","article-title":"Oceanic Rain Flagging Using Radar Backscatter and Noise Measurements from Oceansat-2 Scatterometer","volume":"54","author":"Gohil","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2153","DOI":"10.1109\/TGRS.2015.2496348","article-title":"Unsupervised Learning of Generalized Gamma Mixture Model With Application in Statistical Modeling of High-Resolution SAR Images","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1109\/JSTARS.2017.2747118","article-title":"Mimic Capacity of Fisher and Generalized Gamma Distributions for High-Resolution SAR Image Statistical Modeling","volume":"10","author":"Sportouche","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5436","DOI":"10.1109\/JSTARS.2016.2621818","article-title":"Classification of Detected Changes from Multitemporal High-Resolution X-band SAR Images: Intensity and Texture Descriptors from SuperPixels","volume":"9","author":"Barreto","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1357","DOI":"10.1109\/LGRS.2015.2402391","article-title":"A Comparative Study of Bag-of-Words and Bag-of-Topics Models of EO Image Patches","volume":"12","author":"Bahmanyar","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/LGRS.2016.2628162","article-title":"Airplane Recognition in TerraSAR-X Images via Scatter Cluster Extraction and Reweighted Sparse Representation","volume":"14","author":"Pan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TGRS.2005.859349","article-title":"Dictionary-Based Stochastic Expectation-Maximization for SAR Amplitude Probability Density Function Estimation","volume":"44","author":"Moser","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kayabol, K., Voisin, A., and Zerubia, J. (2011, January 11\u201314). SAR Image Classification with Non-stationary Multinomial Logistic Mixture of Amplitude and Texture Densities. Proceedings of the 18th IEEE International Conference on Image Processing, Brussels, Belgium.","DOI":"10.1109\/ICIP.2011.6115784"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1109\/LGRS.2013.2283258","article-title":"SAR Image Filtering Based on the Cauchy\u2013Rayleigh Mixture Model","volume":"11","author":"Peng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TGRS.2017.2756621","article-title":"Mixture WG \u0393-MRF Model for PolSAR Image Classification","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1007\/s11263-017-1048-0","article-title":"Do Semantic Parts Emerge in Convolutional Neural Networks?","volume":"126","author":"Modolo","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"2351","DOI":"10.1109\/LGRS.2015.2478256","article-title":"High-Resolution SAR Image Classification via Deep Convolutional Autoencoders","volume":"12","author":"Geng","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/TGRS.2016.2551720","article-title":"Target Classification Using the Deep Convolutional Networks for SAR Images","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Huang, Z., Pan, Z., and Lei, B. (2017). Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sens., 9.","DOI":"10.3390\/rs9090907"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep Supervised Learning for Hyperspectral Data Classification through Convolutional Neural Networks. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","article-title":"Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Doulamis, A., Doulamis, N., Nikitakis, A., and Voulodimos, A. (arXiv, 2018). Tensor-based Nonlinear Classifier for High-Order Data Analysis, arXiv.","DOI":"10.1109\/ICASSP.2018.8461418"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qu, J., Lei, J., Li, Y., Dong, W., Zeng, Z., and Chen, D. (2018). Structure Tensor-Based Algorithm for Hyperspectral and Panchromatic Images Fusion. Remote Sens., 10.","DOI":"10.3390\/rs10030373"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1109\/TIP.2018.2815759","article-title":"Supervised Polarimetric SAR Image Classification Using Tensor Local Discriminant Embedding","volume":"27","author":"Huang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1146\/annurev-statistics-010814-020120","article-title":"Learning Deep Generative Models","volume":"2","author":"Salakhutdinov","year":"2015","journal-title":"Ann. Rev. Stat. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3516","DOI":"10.1109\/TGRS.2017.2675902","article-title":"Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification","volume":"55","author":"Zhong","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.neucom.2017.09.065","article-title":"An Overview on Restricted Boltzmann Machines","volume":"275","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Cui, Z., Cao, Z., Yang, J., and Ren, H. (2015). Hierarchical Recognition System for Target Recognition from Sparse Representations. Math. Probl. Eng., 2015.","DOI":"10.1155\/2015\/527095"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1109\/TGRS.2016.2514504","article-title":"POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information","volume":"54","author":"Liu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1080\/2150704X.2016.1258128","article-title":"Object-oriented Ensemble Classification for Polarimetric SAR Imagery Using Restricted Boltzmann Machines","volume":"8","author":"Qin","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.patcog.2016.05.028","article-title":"Discriminant Deep Belief Network for High-Resolution SAR Image Classification","volume":"61","author":"Zhao","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_34","unstructured":"Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C., and Culotta, A. (2009). Implicit Mixtures of Restricted Boltzmann Machines. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.patcog.2013.05.025","article-title":"Training Restricted Boltzmann Machines: An Introduction","volume":"47","author":"Fischer","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1214\/aoms\/1177704481","article-title":"A Generalization of the Gamma Distribution","volume":"33","author":"Stacy","year":"1962","journal-title":"Ann. Math. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/JSTSP.2011.2138675","article-title":"On the Empirical\u2013Statistical Modeling of SAR Images With Generalized Gamma Distribution","volume":"5","author":"Li","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1162\/089976602760128018","article-title":"Training Products of Experts by Minimizing Contrastive Divergence","volume":"14","author":"Hinton","year":"2002","journal-title":"Neural Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fischer, A., and Igel, C. (2010, January 15\u201318). Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines. Proceedings of the 20th International Conference on Artificial Neural Networks, Thessaloniki, Greece.","DOI":"10.1007\/978-3-642-15825-4_26"},{"key":"ref_40","unstructured":"Upadhya, V., and Sastry, P.S. (2017, January 15\u201317). Learning RBM with a DC Programming Approach. Proceedings of the Asian Conference on Machine Learning, Beijing, China."},{"key":"ref_41","unstructured":"Carreira-Perpin\u00e1n, M.A., and Hinton, G. (2005, January 6\u20138). On Contrastive Divergence Learning. Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS), Bridgetown, Barbados."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.tics.2007.09.004","article-title":"Learning Multiple Layers of Representation","volume":"11","author":"Hinton","year":"2007","journal-title":"Trends Cognit. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.1162\/NECO_a_00311","article-title":"An Efficient Learning Procedure for Deep Boltzmann Machines","volume":"24","author":"Salakhutdinov","year":"2012","journal-title":"Neural Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1109\/JSTSP.2010.2103925","article-title":"Supervised High-Resolution Dual-Polarization SAR Image Classification by Finite Mixtures and Copulas","volume":"5","author":"Krylov","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/LGRS.2012.2198610","article-title":"SAR Target Configuration Recognition Using Locality Preserving Property and Gaussian Mixture Distribution","volume":"10","author":"Liu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4391","DOI":"10.1109\/TGRS.2013.2281854","article-title":"Synthetic Aperture Radar Image Segmentation by Modified Student\u2019s t-Mixture Model","volume":"52","author":"Zhang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4232","DOI":"10.1109\/TIP.2012.2199127","article-title":"SAR-Based Terrain Classification Using Weakly Supervised Hierarchical Markov Aspect Models","volume":"21","author":"Yang","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/TIP.2012.2219545","article-title":"Unsupervised Amplitude and Texture Classification of SAR Images With Multinomial Latent Model","volume":"22","author":"Kayabol","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1109\/JSTARS.2013.2293343","article-title":"Nonlinear Compressed Sensing-Based LDA Topic Model for Polarimetric SAR Image Classification","volume":"7","author":"He","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1109\/LGRS.2014.2307952","article-title":"A Comparative Study of Statistical Models for Multilook SAR Images","volume":"11","author":"Cui","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3419","DOI":"10.1109\/JSTARS.2016.2555579","article-title":"Multitemporal SAR Image Decomposition into Strong Scatterers, Background, and Speckle","volume":"9","author":"Lobry","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5467","DOI":"10.1109\/TGRS.2017.2707806","article-title":"Multitemporal SAR Image Despeckling Based on Block-Matching and Collaborative Filtering","volume":"55","author":"Chierchia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1109\/TPAMI.2008.182","article-title":"A Statistical Approach to Material Classification Using Image Patch Exemplars","volume":"31","author":"Varma","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4591","DOI":"10.1109\/TGRS.2013.2265413","article-title":"Information Content of Very High Resolution SAR Images: Study of Feature Extraction and Imaging Parameters","volume":"51","author":"Dumitru","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1162\/neco.2008.04-07-510","article-title":"Representational Power of Restricted Boltzmann Machines and Deep Belief Networks","volume":"20","author":"Roux","year":"2008","journal-title":"Neural Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A Library for Support Vector Machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TIP.2005.864174","article-title":"Invariance Properties of Gabor Filter-Based Features-Overview and Applications","volume":"15","author":"Kamarainen","year":"2006","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/878\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:07:24Z","timestamp":1760195244000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/878"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,5]]},"references-count":60,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060878"],"URL":"https:\/\/doi.org\/10.3390\/rs10060878","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,6,5]]}}}