{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:25:54Z","timestamp":1765268754831,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"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":["61871413","41801236","61801015"],"award-info":[{"award-number":["61871413","41801236","61801015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XK2020-03"],"award-info":[{"award-number":["XK2020-03"]}],"id":[{"id":"10.13039\/501100012226","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) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classification due to their powerful feature learning capabilities. However, a single neuron in the CNN cannot represent multiple polarimetric attributes of the land cover. The capsule network (CapsNet) uses vectors instead of the single neuron to characterize the polarimetric attributes, which improves the classification performance compared with traditional CNNs. In this paper, a hierarchical capsule network (HCapsNet) is proposed for the land cover classification of PolSAR images, which can consider the deep features obtained at different network levels in the classification. Moreover, we adopt three attributes to uniformly describe the scattering mechanisms of different land covers: phase, amplitude, and polarimetric decomposition parameters, which improves the generalization performance of HCapsNet. Furthermore, conditional random field (CRF) is added to the classification framework to eliminate small isolated regions of the intra-class. Comprehensive evaluations are performed on three PolSAR datasets acquired by different sensors, which demonstrate that our proposed method outperforms other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs13163132","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3132","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2410-9778","authenticated-orcid":false,"given":"Jianda","family":"Cheng","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-2373","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0152-6621","authenticated-orcid":false,"given":"Deliang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China"},{"name":"Interdisciplinary Research Center for Artificial Intelligence, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Qiang","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7261-7606","authenticated-orcid":false,"given":"Yongsheng","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, F., Ni, J., Yin, Q., Li, W., Li, Z., Liu, Y., and Hong, W. (2017). Nearest-regularized subspace classification for PolSAR imagery using polarimetric feature vector and spatial information. Remote Sens., 9.","DOI":"10.3390\/rs9111114"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.cageo.2018.01.018","article-title":"Building damage assessment from PolSAR data using texture parameters of statistical model","volume":"113","author":"Li","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/S0925-2312(99)00056-9","article-title":"Fuzzy clustering approach in unsupervised sea-ice classification","volume":"25","author":"Eom","year":"1999","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neucom.2016.08.140","article-title":"Fully PolSAR image classification using machine learning techniques and reaction-diffusion systems","volume":"255","author":"Gomez","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1109\/TGRS.2017.2662010","article-title":"Adaptive superpixel generation for polarimetric SAR images with local iterative clustering and SIRV model","volume":"55","author":"Xiang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/LGRS.2018.2821711","article-title":"SAR image classification by exploiting adaptive contextual information and composite kernels","volume":"15","author":"Guan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173826","DOI":"10.1109\/ACCESS.2020.3023134","article-title":"Interpretable POLSAR Image Classification Based on Adaptive-dimension Feature Space Decision Tree","volume":"8","author":"Yin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6601","DOI":"10.1109\/TIP.2020.2992177","article-title":"Polarimetric SAR image semantic segmentation with 3D discrete wavelet transform and Markov random field","volume":"29","author":"Bi","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","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":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4244","DOI":"10.1109\/JSTARS.2018.2866407","article-title":"A novel phenology based feature subset selection technique using random forest for multitemporal PolSAR crop classification","volume":"11","author":"Hariharan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, Y., Liu, G., and Jiao, L. (2018). A novel deep fully convolutional network for PolSAR image classification. Remote Sens., 10.","DOI":"10.3390\/rs10121984"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.neucom.2020.01.020","article-title":"PolSAR image classification via a novel semi-supervised recurrent complex-valued convolution neural network","volume":"388","author":"Xie","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1109\/TGRS.2006.886176","article-title":"Target scattering decomposition in terms of roll-invariant target parameters","volume":"45","author":"Touzi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"491","DOI":"10.5589\/m14-002","article-title":"Polarimetric Radarsat-2 wetland classification using the Touzi decomposition: Case of the Lac Saint-Pierre Ramsar wetland","volume":"39","author":"Gosselin","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4452","DOI":"10.1109\/JSTARS.2018.2873740","article-title":"Scattered and received wave polarization optimization for enhanced peatland classification and fire damage assessment using polarimetric PALSAR","volume":"11","author":"Touzi","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111234","DOI":"10.1016\/j.rse.2019.111234","article-title":"Crop phenology retrieval via polarimetric SAR decomposition and Random Forest algorithm","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3213","DOI":"10.1109\/JSTARS.2017.2681719","article-title":"Scattering mechanism based snow cover mapping using RADARSAT-2 C-Band polarimetric SAR data","volume":"10","author":"Muhuri","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1109\/JSTARS.2019.2909984","article-title":"Refining a polarimetric decomposition of multi-angular UAVSAR time series for soil moisture retrieval over low and high vegetated agricultural fields","volume":"12","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, J.w., Ding, X.h., Lu, R.k., Lian, Y.f., Wang, D.z., and Luo, X.l. (2019). Multi-View Capsule Network. International Conference on Artificial Neural Networks, Springer.","DOI":"10.1007\/978-3-030-30487-4_13"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85007","DOI":"10.1109\/ACCESS.2020.2992655","article-title":"RS-CapsNet: An Advanced Capsule Network","volume":"8","author":"Yang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"85492","DOI":"10.1109\/ACCESS.2019.2924548","article-title":"Cv-CapsNet: Complex-valued capsule network","volume":"7","author":"Cheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","unstructured":"Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic routing between capsules. arXiv."},{"key":"ref_24","unstructured":"Hinton, G.E., Sabour, S., and Frosst, N. (May, January 30). Matrix capsules with EM routing. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4663","DOI":"10.1109\/JSTARS.2020.3015909","article-title":"Learning Capsules for SAR Target Recognition","volume":"13","author":"Guo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","unstructured":"Phaye, S.S.R., Sikka, A., Dhall, A., and Bathula, D. (2018). Dense and diverse capsule networks: Making the capsules learn better. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, A., Wang, M., Wu, H., Jiang, K., and Iwahori, Y. (2020). A Novel LiDAR Data Classification Algorithm Combined CapsNet with ResNet. Sensors, 20.","DOI":"10.3390\/s20041151"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L. (2019). Remote sensing image scene classification using CNN-CapsNet. Remote Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ma, W., Xiong, Y., Wu, Y., Yang, H., Zhang, X., and Jiao, L. (2019). Change detection in remote sensing images based on image mapping and a deep capsule network. Remote Sens., 11.","DOI":"10.3390\/rs11060626"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, K., Chen, Y., Ghamisi, P., Jia, X., and Benediktsson, J.A. (2019). Deep convolutional capsule network for hyperspectral image spectral and spectral-spatial classification. Remote Sens., 11.","DOI":"10.3390\/rs11030223"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, F., Pu, S., Chen, X., Shi, Y., Yuan, T., and Pu, S. (2018). Hyperspectral image classification with capsule network using limited training samples. Sensors, 18.","DOI":"10.3390\/s18093153"},{"key":"ref_32","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_33","unstructured":"Lafferty, J., McCallum, A., and Pereira, F.C. (July, January 28). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the 18th International Conference on Machine Learning 2001 (ICML 2001), Williamstown, MA, USA."},{"key":"ref_34","first-page":"109","article-title":"Efficient inference in fully connected crfs with gaussian edge potentials","volume":"24","author":"Koltun","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8914","DOI":"10.1109\/TGRS.2019.2923738","article-title":"Polar-Spatial Feature Fusion Learning With Variational Generative-Discriminative Network for PolSAR Classification","volume":"57","author":"Wen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, S., Xu, Z., Zhang, C., Zhang, J., Mu, Z., Zhao, T., Wang, Y., Gao, S., Yin, H., and Zhang, Z. (2020). Improved winter wheat spatial distribution extraction using a convolutional neural network and partly connected conditional random field. Remote Sens., 12.","DOI":"10.3390\/rs12050821"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"24070","DOI":"10.1109\/ACCESS.2017.2768575","article-title":"Context-based max-margin for PolSAR image classification","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_38","unstructured":"Ziegler, V., L\u00fcneburg, E., and Schroth, A. Mean backscattering properties of random radar targets-A polarimetric covariance matrix concept. Proceedings of the IGARSS\u201992; Proceedings of the 12th Annual International Geoscience and Remote Sensing Symposium, Houston, TX, USA, 26\u201329 May 1992."},{"key":"ref_39","unstructured":"Buckley, J.R. (2002, January 24\u201328). Environmental change detection in prairie landscapes with simulated RADARSAT 2 imagery. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","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":"Rauste","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2010\/465612","article-title":"Polarimetric SAR image classification using multifeatures combination and extremely randomized clustering forests","volume":"2010","author":"Zou","year":"2009","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2116","DOI":"10.1109\/TGRS.2018.2871504","article-title":"A graph-based semisupervised deep learning model for PolSAR image classification","volume":"57","author":"Bi","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8583","DOI":"10.1109\/TGRS.2020.2988982","article-title":"Semi-Supervised PolSAR Image Classification Based on Improved Tri-Training With a Minimum Spanning Tree","volume":"58","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100794","DOI":"10.1016\/j.swevo.2020.100794","article-title":"Multiobjective Evolutionary Algorithm Assisted Stacked Autoencoder for PolSAR Image Classification","volume":"60","author":"Liu","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"107110","DOI":"10.1016\/j.patcog.2019.107110","article-title":"Complex contourlet-CNN for polarimetric SAR image classification","volume":"100","author":"Li","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"840","DOI":"10.18178\/ijmlc.2019.9.6.881","article-title":"Capsnet, cnn, fcn: Comparative performance evaluation for image classification","volume":"9","author":"Jiang","year":"2019","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11263-007-0109-1","article-title":"Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context","volume":"81","author":"Shotton","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","unstructured":"Liu, X., Jiao, L., and Liu, F. (2019). PolSF: PolSAR image dataset on San Francisco. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wang, Y., Cheng, J., Zhou, Y., Zhang, F., and Yin, Q. (2021). A Multichannel Fusion Convolutional Neural Network Based on Scattering Mechanism for PolSAR Image Classification. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2020.3047635"},{"key":"ref_53","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_54","unstructured":"Zhang, F., Yan, M., Hu, C., Ni, J., and Ma, F. (2020). The global information for land cover classification by dual-branch deep learning. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3497","DOI":"10.1109\/JSTARS.2015.2420683","article-title":"Modifying the Yamaguchi four-component decomposition scattering powers using a stochastic distance","volume":"8","author":"Bhattacharya","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"43","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1080\/15481603.2020.1853948","article-title":"Classification of polarimetric SAR images using compact convolutional neural networks","volume":"58","author":"Ahishali","year":"2020","journal-title":"GISci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1109\/LGRS.2020.2990711","article-title":"Composite Kernel and Hybrid Discriminative Random Field Model Based on Feature Fusion for PolSAR Image Classification","volume":"18","author":"Song","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.3390\/rs6032008","article-title":"Identification of soil freezing and thawing states using SAR polarimetry at C-band","volume":"6","author":"Jagdhuber","year":"2014","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"17135","DOI":"10.3390\/rs71215874","article-title":"Variations of microwave scattering properties by seasonal freeze\/thaw transition in the permafrost active layer observed by ALOS PALSAR polarimetric data","volume":"7","author":"Park","year":"2015","journal-title":"Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.1109\/JSTARS.2018.2817687","article-title":"Snow cover mapping using polarization fraction variation with temporal RADARSAT-2 C-band full-polarimetric SAR data over the Indian Himalayas","volume":"11","author":"Muhuri","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3132\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:42:14Z","timestamp":1760164934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"references-count":61,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163132"],"URL":"https:\/\/doi.org\/10.3390\/rs13163132","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,8,7]]}}}