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ISPRS Int. J. Geo-Inf. 2018, 7(2), 74;

Classification of PolSAR Images by Stacked Random Forests

Computer Vision & Remote Sensing, Technische Universität Berlin, Berlin 10587, Germany
Author to whom correspondence should be addressed.
Received: 30 January 2018 / Revised: 16 February 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
PDF [44702 KB, uploaded 23 February 2018]


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. View Full-Text
Keywords: random forests; stacking; ensemble learning; classification; PolSAR random forests; stacking; ensemble learning; classification; PolSAR

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Hänsch, R.; Hellwich, O. Classification of PolSAR Images by Stacked Random Forests. ISPRS Int. J. Geo-Inf. 2018, 7, 74.

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