{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:12:05Z","timestamp":1760209925157,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,14]],"date-time":"2017-09-14T00:00:00Z","timestamp":1505347200000},"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>In this paper, we propose a classification scheme for forest growth stage types and other cover types using a support vector machine (SVM) based on the Polarimetric SAR Interferometric (PolInSAR) data acquired by Chinese Multidimensional Space Joint-observation SAR (MSJosSAR) system. Firstly, polarimetric, texture, and coherence features were calculated from the PolInSAR data. Secondly, the capabilities of the polarimetric, texture, and coherence features in land use\/cover classification were quantified independently through histograms. Following this, the polarimetric features were used for the classification of land use\/cover types, followed by a combination of texture and coherence features. Finally, the three classification results were validated against test samples using the confusion matrix. It was shown that, with the integration of texture and coherence features, the producer\u2019s accuracy for afforested land, young forest land, medium forest land, and near-mature forest land improved by 6%, 31%, 11%, and 6%, respectively, compared with the former experiment using solely polarimetric features. Our study indicates that the forest and non-forest lands can be discriminated by the polarimetric features, which also play an important role in the separation between afforested land and other forest types as well as medium forest land and near-mature forest land. The texture features further discriminate afforested land and other forest types, while the coherence features obviously improved the separation of young forest land and medium forest land. This paper provides an effective way of identifying various land use\/cover types, especially for distinguishing forest growth stages with SAR data. It would be of great interest in regions with frequent cloud coverage and limited optical data for the monitoring of land use\/cover types.<\/jats:p>","DOI":"10.3390\/rs9090955","type":"journal-article","created":{"date-parts":[[2017,9,15]],"date-time":"2017-09-15T10:22:10Z","timestamp":1505470930000},"page":"955","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["The Performance of Airborne C-Band PolInSAR Data on Forest Growth Stage Types Classification"],"prefix":"10.3390","volume":"9","author":[{"given":"Qi","family":"Feng","sequence":"first","affiliation":[{"name":"Sciences and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangjiang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Sciences and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erxue","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingdong","family":"Liang","sequence":"additional","affiliation":[{"name":"Sciences and Technology on Microwave Imaging Laboratory, Institute of Electronics, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5544-8342","authenticated-orcid":false,"given":"Yu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Graduate School of Geography, Clark University, Worcester, MA 01610, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"West, P. (2009). Tree and Forest Measurement, Springer. [2nd ed.].","DOI":"10.1007\/978-3-540-95966-3"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2013.11.025","article-title":"Mapping forest growth and degradation stage in the brigalow belt bioregion of australia through integration of alos palsar and landsat-derived foliage projective cover data","volume":"155","author":"Lucas","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"8","article-title":"Possibilities of discriminating tropical secondary succession in Amazonia using hyperspectral and multiangular CHRIS\/PROBA data","volume":"11","author":"Galvao","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/19479832.2010.495323","article-title":"Discrimination of peatlands in tropical swamp forests using dual-polarimetric SAR and Landsat ETM data","volume":"1","author":"Wijaya","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.foreco.2006.05.066","article-title":"Classifying regenerating forest stages in Amazonia using remotely sensed images and a neural network","volume":"234","author":"Kuplich","year":"2006","journal-title":"For. Ecol. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.3724\/SP.J.1300.2014.13089","article-title":"Improved Three-stage Algorithm of Forest Height Retrieval with PolInSAR","volume":"3","author":"Xu","year":"2014","journal-title":"J. Radars"},{"key":"ref_7","first-page":"136","article-title":"Deep Learning as Applied in SAR Target Recognition and Terrain Classification","volume":"6","author":"Xu","year":"2017","journal-title":"J. Radars"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"339","DOI":"10.3724\/SP.J.1300.2014.13055","article-title":"Applications of Environmental Remote Sensing by HJ-1C SAR Imageries","volume":"3","author":"Tian","year":"2014","journal-title":"J. Radars"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1109\/36.868888","article-title":"The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest","volume":"38","author":"Simard","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ningthoujam, R.K., Tansey, K., Balzter, H., Morrison, K., Johnson, S.C.M., Gerard, F., George, C., Burbidge, G., Doody, S., and Veck, N. (2016). Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar. Remote Sens., 8.","DOI":"10.3390\/rs8070577"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/s12518-010-0030-0","article-title":"Erratum to: Mapping Tropical forest cover and deforestation using Synthetic Aperture Radar (SAR) Images","volume":"2","author":"Rahman","year":"2010","journal-title":"Appl. Geomat."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TGRS.2011.2171495","article-title":"Joint processing of Landsat and ALOS-PALSAR data for forest mapping and monitoring","volume":"50","author":"Lehmann","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2135","DOI":"10.1109\/TGRS.2010.2102041","article-title":"Assessment of ALOS PALSAR 50 m orthorectified FBD data for regional land cover classification by Support Vector Machines","volume":"49","author":"Longepe","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.rse.2008.11.004","article-title":"Use of multitemporal SAR data for the monitoring of the vegetation recovery in burned areas","volume":"113","author":"Minchella","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_15","unstructured":"Lee, J.S., Papathanassiou, K.P., Hajnsek, I., and Mette, T. (2005, January 29). Applying polarimetric SAR interferometric data for forest classification. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2005), Seoul, Korea."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1288","DOI":"10.1109\/TGRS.2002.800242","article-title":"Biophysical forest type characterization in the Colombian Amazon by airborne polarimetric SAR","volume":"40","author":"Hoekman","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/36.823949","article-title":"Monitoring seasonal changes of a mixed temperature forest using ERS SAR observations","volume":"38","author":"Proisy","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"543","DOI":"10.5589\/m03-072","article-title":"Forest type discrimination using calibrated C-band polarimetric SAR data","volume":"30","author":"Touzi","year":"2004","journal-title":"Can. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1109\/JSTARS.2013.2259219","article-title":"Radarsat-2 Polarimetric SAR Data for Boreal Forest Classification Using SVM and a Wrapper Feature Selector","volume":"6","author":"Maghsoudi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","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_22","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":"Chinatsu","year":"2012","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3390\/rs2040939","article-title":"Eucalyptus biomass and volume estimation using interferometric and polarimetric SAR data","volume":"2","author":"Gama","year":"2010","journal-title":"Remote Sens."},{"key":"ref_24","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_25","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_26","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2003.819883","article-title":"Unsupervised terrain classification preserving scattering characteristics","volume":"42","author":"Lee","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"Ferro Famil, L., Pottier, E., and Lee, J.S. (2001, January 9\u201313). Unsupervised classification of natural scenes from polarimetric interferometric SAR data. Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium (IGARSS 2001), Sydney, Australia."},{"key":"ref_28","unstructured":"Ferro-Famil, L., Kugler, F., Potier, E., and Lee, J.S. (2006, January 16\u201318). Forest mapping and classification at L-Band using Pol-InSAR optimal coherence set statistics. Proceedings of the European Conference on Synthetic Aperture Radar, Dresden, Germany."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5447","DOI":"10.1080\/01431160701227596","article-title":"Land-Cover Classification in the Brazilian Amazon with the Integration of Landsat ETM+ and RADARSAT Data","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"7","article-title":"Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in west Africa","volume":"21","author":"Laurin","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, G., Li, L., Gong, H., Jin, Q.W., Li, X.W., Song, R., Chen, Y., Chen, Y., He, C.X., and Huang, Y.Q. (2017). Multisource Remote Sensing Imagery Fusion Scheme Based on Bidimensional Empirical Mode Decomposition (BEMD) and Its Application to the Extraction of Bamboo Forest. Remote Sens., 9.","DOI":"10.3390\/rs9010019"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.isprsjprs.2012.03.010","article-title":"A Comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region","volume":"70","author":"Li","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.3390\/rs5031001","article-title":"Impacts of spatial variability on aboveground biomass estimation from L-Band radar in a temperate forest","volume":"5","author":"Robinson","year":"2013","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (2006). Estimation of Dependences Based on Empirical Data, Springer.","DOI":"10.1007\/0-387-34239-7"},{"key":"ref_35","first-page":"15","article-title":"A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods","volume":"27","author":"Lin","year":"2003","journal-title":"Neural Comput."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1467","DOI":"10.1109\/JSTARS.2016.2621043","article-title":"Contributions of c-band SAR data and polarimetric decompositions to subarctic boreal peatland mapping","volume":"10","author":"Merchant","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Doulgeris, A. (2011). Non-Gaussian Statistical Analysis of Polarimetric Synthetic Aperture Radar Images. [Ph.D. Thesis, University of Troms\u00f8 (UIT)].","DOI":"10.1109\/TGRS.2011.2140120"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1109\/36.964971","article-title":"Single-baseline polarimetric SAR interferometry","volume":"39","author":"Papathanassiou","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Tison, C., Pourthi\u00e9, N., and Souyris, J.C. (2007, January 23\u201328). Target recognition in SAR images with support vector machine (SVM). Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2007), Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4422829"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Mercier, G., and Girard-Ardhuin, F. (2005, January 29). Unsupervised oil slick detection by SAR imagery using kernel expansion. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2005), Seoul, Korea.","DOI":"10.1109\/OCEANSE.2005.1511690"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3624","DOI":"10.3390\/rs6053624","article-title":"Classifying Complex Mountainous Forests with L-Band SAR and Landsat Data Integration: A Comparison among Different Machine Learning Methods in the Hyrcanian Forest","volume":"6","author":"Attarchi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_42","unstructured":"Fayyad, U. (1998). A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery, Kluwer."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2727","DOI":"10.1109\/TGRS.2014.2364076","article-title":"Characterization of facade regularities in high-resolution SAR images","volume":"53","author":"Auer","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0378-1127(02)00028-2","article-title":"Forest woody biomass classification with satellite-based radar coherence over 900,000 km2, in Central Siberia","volume":"174","author":"Gaveau","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1109\/TGRS.2005.846878","article-title":"Multitemporal repeat pass SAR interferometry of boreal forests","volume":"43","author":"Askne","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","unstructured":"Cartus, O., Santoro, M., and Schmullius, C. (2008, January 21\u201325). Creation of large area forest biomass maps for Northeast China using ERS-1\/2 tandem coherence. Proceedings of the Dragon 1 Programme Final Results 2004\u20132007, Beijing, China."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/5.838084","article-title":"Synthetic aperture radar interferometry","volume":"88","author":"Rosen","year":"2000","journal-title":"Proc. IEEE"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/0266-5611\/14\/4\/001","article-title":"Synthetic aperture radar interferometry","volume":"14","author":"Bamler","year":"1998","journal-title":"Inverse Probl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1029\/96RS01763","article-title":"Vegetation characteristics and underlying topography from interferometric radar","volume":"31","author":"Treuhaft","year":"1996","journal-title":"Radio Sci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/36.551931","article-title":"C-band repeat-pass interferometric SAR observations of the forest","volume":"35","author":"Askne","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/955\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:44:58Z","timestamp":1760208298000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,14]]},"references-count":50,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090955"],"URL":"https:\/\/doi.org\/10.3390\/rs9090955","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2017,9,14]]}}}