Classification of PolSAR Images by Stacked Random Forests
AbstractThis 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
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Hänsch, R.; Hellwich, O. Classification of PolSAR Images by Stacked Random Forests. ISPRS Int. J. Geo-Inf. 2018, 7, 74.
Hänsch R, Hellwich O. Classification of PolSAR Images by Stacked Random Forests. ISPRS International Journal of Geo-Information. 2018; 7(2):74.Chicago/Turabian Style
Hänsch, Ronny; Hellwich, Olaf. 2018. "Classification of PolSAR Images by Stacked Random Forests." ISPRS Int. J. Geo-Inf. 7, no. 2: 74.
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