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Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features

Lookup NU author(s): Professor Zhenhong Li


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Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters can’t provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and Support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison in the experiments. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest.

Publication metadata

Author(s): Du P, Samat A, Waske B, Liu S, Li Z

Publication type: Article

Publication status: Published

Journal: ISPRS Journal of Photogrammetry and Remote Sensing

Year: 2015

Volume: 105

Pages: 38-53

Print publication date: 01/07/2015

Online publication date: 13/04/2015

Acceptance date: 03/03/2015

ISSN (print): 0924-2716

ISSN (electronic): 1872-8235

Publisher: Elsevier


DOI: 10.1016/j.isprsjprs.2015.03.002

Notes: Highly cited paper in WoS.


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