{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T20:40:58Z","timestamp":1769719258761,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,23]],"date-time":"2018-02-23T00:00:00Z","timestamp":1519344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To this aim, the internal node tests are designed to work not only directly on the complex-valued image data, but also on spatially varying probability distributions and thus allow a seamless integration of RFs within the stacking framework. Experimental results show that the classification performance is consistently improved by the proposed approach, i.e., the achieved accuracy is increased by 4 % and 7 % for one fully- and one dual-polarimetric dataset. This increase only comes at the cost of a linear increased training and prediction time, which is rather limited as the method converges quickly.<\/jats:p>","DOI":"10.3390\/ijgi7020074","type":"journal-article","created":{"date-parts":[[2018,2,23]],"date-time":"2018-02-23T11:31:36Z","timestamp":1519385496000},"page":"74","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Classification of PolSAR Images by Stacked Random Forests"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2936-6765","authenticated-orcid":false,"given":"Ronny","family":"H\u00e4nsch","sequence":"first","affiliation":[{"name":"Computer Vision & Remote Sensing, Technische Universit\u00e4t Berlin, Berlin 10587, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olaf","family":"Hellwich","sequence":"additional","affiliation":[{"name":"Computer Vision & Remote Sensing, Technische Universit\u00e4t Berlin, Berlin 10587, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TGRS.2004.834630","article-title":"A new statistical model for Markovian classification of urban areas in high-resolution SAR images","volume":"42","author":"Tison","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","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_3","doi-asserted-by":"crossref","unstructured":"Nicolas, J.M., and Tupin, F. (September, January 28). Statistical models for SAR amplitude data: A unified vision through Mellin transform and Meijer functions. Proceedings of the 2016 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary.","DOI":"10.1109\/EUSIPCO.2016.7760302"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TGRS.2004.842022","article-title":"Partially Supervised classification of remote sensing images through SVM-based probability density estimation","volume":"43","author":"Mantero","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1109\/TGRS.2004.826821","article-title":"An advanced system for the automatic classification of multitemporal SAR images","volume":"42","author":"Bruzzone","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"H\u00e4nsch, R., and Hellwich, O. (2010, January 25\u201330). Random Forests for building detection in polarimetric SAR data. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652539"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"4576","DOI":"10.1109\/TGRS.2012.2236338","article-title":"Texture Classification of PolSAR Data Based on Sparse Coding of Wavelet Polarization Textons","volume":"51","author":"He","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"1081","article-title":"Complex-Valued Multi-Layer Perceptrons\u2014An Application to Polarimetric SAR Data","volume":"9","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Moser, G., and Serpico, S.B. (2014, January 13\u201318). Kernel-based classification in complex-valued feature spaces for polarimetric SAR data. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946661"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1016\/j.patcog.2013.11.009","article-title":"A novel approach to polarimetric SAR data processing based on Nonlinear PCA","volume":"47","author":"Licciardi","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TGRS.2014.2360943","article-title":"Tensorial Independent Component Analysis-Based Feature Extraction for Polarimetric SAR Data Classification","volume":"53","author":"Tao","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","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 Observ. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_15","unstructured":"H\u00e4nsch, R. (2014). Generic Object Categorization in PolSAR Images-and Beyond. [Ph.D. Thesis, Technical University of Berlin]."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1109\/TGRS.2014.2321423","article-title":"Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images","volume":"53","author":"Tokarczyk","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","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_18","unstructured":"H\u00e4nsch, R., and Hellwich, O. (2010, January 7\u201310). Complex-Valued Convolutional Neural Networks for Object Detection in PolSAR data. Proceedings of the 8th European Conference on Synthetic Aperture Radar, Aachen, Germany."},{"key":"ref_19","first-page":"1","article-title":"Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification","volume":"PP","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Criminisi, A., and Shotton, J. (2013). Decision Forests for Computer Vision and Medical Image Analysis, Springer.","DOI":"10.1007\/978-1-4471-4929-3"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fr\u00f6hlich, B., Rodner, E., and Denzler, J. (2012, January 5\u20139). Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach. Proceedings of the 11th Asian Conference on Computer Vision, Daejeon, Korea.","DOI":"10.1007\/978-3-642-37331-2_17"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"H\u00e4nsch, R., and Hellwich, O. (2017). Skipping the real world: Classification of PolSAR images without explicit feature extraction. J. Photogramm. Remote Sens.","DOI":"10.1016\/j.isprsjprs.2017.11.022"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked Generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/BF00117832","article-title":"Stacked regressions","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"van der Laan, M.J., Polley, E.C., and Hubbard, A.E. (2007). Super Learner, U.C. Berkeley.","DOI":"10.2202\/1544-6115.1309"},{"key":"ref_26","first-page":"1","article-title":"The BigChaos Solution to the Netflix Grand Prize","volume":"81","author":"Jahrer","year":"2009","journal-title":"Netflix Prize Doc."},{"key":"ref_27","unstructured":"Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications, Taylor & Francis."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1214\/aoms\/1177704250","article-title":"Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction)","volume":"34","author":"Goodman","year":"1963","journal-title":"Ann. Math. Stat."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/01431169408954244","article-title":"Classification of multilook polarimetric SAR imagery based on complex Wishart distribution","volume":"15","author":"Lee","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","unstructured":"Anfinsen, S.N., Jenssen, R., and Eltoft, T. (2007, January 22\u201326). Spectral clustering of polarimetric SAR data with Wishart-derived distance measures. Proceedings of the 7th POLinSAR, Frascati, Italy."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TGRS.2002.808066","article-title":"A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data","volume":"41","author":"Conradsen","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Barbaresco, F. (2009). Interactions between symmetric cone and information geometries: Bruhat-Tits and Siegel spaces models for high resolution autoregressive doppler imagery. Emerg. Trends Visual Comput., 124\u2013163.","DOI":"10.1007\/978-3-642-00826-9_6"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1002\/mrm.20965","article-title":"Log-Euclidean metrics for fast and simple calculus on diffusion tensors","volume":"56","author":"Arsigny","year":"2006","journal-title":"Magn. Reson. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"H\u00e4nsch, R., and Hellwich, O. (2015, January 26\u201331). Evaluation of tree creation methods within Random Forests for classification of PolSAR images. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325775"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1465","DOI":"10.1109\/TPAMI.2006.188","article-title":"Keypoint Recognition Using Randomized Trees","volume":"28","author":"Lepetit","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/2\/74\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:56:05Z","timestamp":1760194565000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/2\/74"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,2,23]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["ijgi7020074"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7020074","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,2,23]]}}}