{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T09:15:18Z","timestamp":1768554918915,"version":"3.49.0"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,3,31]],"date-time":"2019-03-31T00:00:00Z","timestamp":1553990400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canadian Space Agency SOAR-E program","award":["5489"],"award-info":[{"award-number":["5489"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41804004"],"award-info":[{"award-number":["41804004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41820104005"],"award-info":[{"award-number":["41820104005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41531068"],"award-info":[{"award-number":["41531068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["162301182689"],"award-info":[{"award-number":["162301182689"]}]},{"name":"Spanish Ministry of Science, Innovation and Universities, State Research Agency (AEI) and the European Regional Development Fund","award":["TEC2017-85244-C2-1-P"],"award-info":[{"award-number":["TEC2017-85244-C2-1-P"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single\/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude\u2013Pottier decomposition (or \u201cH\/A\/\u03b1\u201d) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle \u03b1 in Cloude\u2013Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named \u201cND-RF\u201d) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude\u2013Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude\u2013Pottier decomposition.<\/jats:p>","DOI":"10.3390\/rs11070776","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T03:21:26Z","timestamp":1554175286000},"page":"776","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-3354","authenticated-orcid":false,"given":"Qinghua","family":"Xie","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Department of Geography, The University of Western Ontario, London, ON N6A 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-0530","authenticated-orcid":false,"given":"Jinfei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Western Ontario, London, ON N6A 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5504-206X","authenticated-orcid":false,"given":"Chunhua","family":"Liao","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Western Ontario, London, ON N6A 5C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiali","family":"Shang","sequence":"additional","affiliation":[{"name":"Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4216-5175","authenticated-orcid":false,"given":"Juan M.","family":"Lopez-Sanchez","sequence":"additional","affiliation":[{"name":"Institute for Computer Research (IUII), University of Alicante, E-03080 Alicante, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiuguo","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,31]]},"reference":[{"key":"ref_1","unstructured":"Brown, L.R. (2005). Outgrowing the Earth: The Food Security Challenge in an Age of Falling Water Tables and Rising Temperatures, W. W. Norton & Company."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TGRS.2009.2026052","article-title":"The contribution of ALOS PALSAR multipolarization and polarimetric data to crop classification","volume":"47","author":"McNairn","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","unstructured":"Woodhouse, I.H. (2006). Introduction to Microwave Remote Sensing, CRC Press."},{"key":"ref_4","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, CRC Press."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cloude, S.R. (2009). Polarisation: Applications in Remote Sensing, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780199569731.001.0001"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/JSTARS.2016.2639043","article-title":"Radar Remote Sensing of Agricultural Canopies: A Review","volume":"10","author":"McNairn","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2017.02.014","article-title":"Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery","volume":"193","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gao, H., Wang, C., Wang, G., Zhu, J., Tang, Y., Shen, P., and Zhu, Z. (2018). A Crop Classification Method Integrating GF-3 PolSAR and Sentinel-2A Optical Data in the Dongting Lake Basin. Sensors, 18.","DOI":"10.3390\/s18093139"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"660","DOI":"10.3390\/rs9070660","article-title":"PolSAR Land Cover Classification Based on Roll-Invariant and Selected Hidden Polarimetric Features in the Rotation Domain","volume":"9","author":"Tao","year":"2017","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5232","DOI":"10.1080\/01431161.2017.1335912","article-title":"Weighted Wishart distance learning for PolSAR image classification","volume":"38","author":"Sun","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"4233","DOI":"10.1080\/01431161.2015.1079345","article-title":"Crop discrimination based on polarimetric correlation coefficients optimization for PolSAR data","volume":"36","author":"Chen","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TGRS.2011.2172994","article-title":"Crop classification by multitemporal C- and L-band single- and dual-polarization and fully polarimetric SAR","volume":"50","author":"Skriver","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSTARS.2011.2106198","article-title":"Crop Classification Using Short-Revisit Multitemporal SAR Data","volume":"4","author":"Skriver","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"60","DOI":"10.5589\/m12-012","article-title":"Towards operational radar-only crop type classification: Comparison of a traditional decision tree with a random forest classifier","volume":"38","author":"Deschamps","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/2150704X.2014.889863","article-title":"Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data","volume":"5","author":"Sonobe","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/07038992.2018.1481737","article-title":"Contribution of Minimum Noise Fraction Transformation of Multi-temporal RADARSAT-2 Polarimetric SAR Data to Cropland Classification","volume":"44","author":"Liao","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","first-page":"1","article-title":"A study on vegetation cover extraction using a Wishart H-\u03b1 classifier based on fully polarimetric Radarsat-2 data","volume":"1161","author":"Li","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.06.014","article-title":"Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data","volume":"96","author":"Jiao","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","first-page":"169","article-title":"Fusion of PolSAR and PolInSAR data for land cover classification","volume":"11","author":"Shimoni","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/MSP.2014.2312099","article-title":"Modeling and interpretation of scattering mechanisms in polarimetric synthetic aperture radar: Advances and perspectives","volume":"31","author":"Chen","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TGRS.2010.2099124","article-title":"Four-Component Scattering Power Decomposition with Rotation of Coherency Matrix","volume":"49","author":"Yamaguchi","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1109\/LGRS.2011.2162935","article-title":"Sang-Eun Park Four-Component Scattering Power Decomposition with Extended Volume Scattering Model","volume":"9","author":"Sato","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3014","DOI":"10.1109\/TGRS.2012.2212446","article-title":"General Four-Component Scattering Power Decomposition with Unitary Transformation of Coherency Matrix","volume":"51","author":"Singh","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1109\/TGRS.2013.2255615","article-title":"General Polarimetric Model-Based Decomposition for Coherency Matrix","volume":"52","author":"Chen","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","unstructured":"Xiang, D., Ban, Y., and Su, Y. (2015). Model-Based Decomposition with Cross Scattering for Polarimetric SAR Urban Areas. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1049\/iet-rsn.2016.0105","article-title":"Multiple-component polarimetric decomposition with new volume scattering models for PolSAR urban areas","volume":"11","author":"Xiang","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xie, Q., Ballester-Berman, J.D., Lopez-Sanchez, J.M., Zhu, J., and Wang, C. (2016). Quantitative Analysis of Polarimetric Model-Based Decomposition Methods. Remote Sens., 8.","DOI":"10.3390\/rs8120977"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xie, Q., Ballester-Berman, J.D., Lopez-Sanchez, J.M., Zhu, J., and Wang, C. (2017). On the Use of Generalized Volume Scattering Models for the Improvement of General Polarimetric Model-Based Decomposition. Remote Sens., 9.","DOI":"10.3390\/rs9020117"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1109\/LGRS.2018.2830503","article-title":"A Modified General Polarimetric Model-Based Decomposition Method with the Simplified Neumann Volume Scattering Model","volume":"15","author":"Xie","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1109\/36.789621","article-title":"Unsupervised classification using polarimetric decomposition and the complex Wishart classifier","volume":"37","author":"Lee","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1109\/36.964969","article-title":"Unsupervised classification of multifrequency and fully polarimetric SAR images based on the H\/A\/Alpha-Wishart classifier","volume":"39","author":"Pottier","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","unstructured":"Neumann, M. (2009). Remote sensing of Vegetation Using Multi-Baseline Polarimetric SAR Interferometry: Theoretical Modeling and Physical Parameter Retrieval. [Ph.D. Thesis, University of Rennes]."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Neumann, M., Ferro-Famil, L., Jager, M., Reigber, A., and Pottier, E. (2009). A polarimetric vegetation model to retrieve particle and orientation distribution characteristics. 2009 IEEE International Geoscience and Remote Sensing Symposium, IEEE.","DOI":"10.1109\/IGARSS.2009.5417351"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1109\/TGRS.2009.2031101","article-title":"Estimation of Forest Structure, Ground, and Canopy Layer Characteristics from Multibaseline Polarimetric Interferometric SAR Data","volume":"48","author":"Neumann","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1109\/TGRS.2011.2176133","article-title":"Assessing Performance of L- and P-Band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-Ground Biomass","volume":"50","author":"Neumann","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1109\/TGRS.2010.2048333","article-title":"The Effect of Orientation Angle Compensation on Coherency Matrix and Polarimetric Target Decompositions","volume":"49","author":"Lee","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/776\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:41:57Z","timestamp":1760186517000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/7\/776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,31]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2019,4]]}},"alternative-id":["rs11070776"],"URL":"https:\/\/doi.org\/10.3390\/rs11070776","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,31]]}}}