Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-8972
Main Title: Classification of PolSAR Images by Stacked Random Forests
Author(s): Hänsch, Ronny
Hellwich, Olaf
Type: Article
Language Code: en
Abstract: 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.
URI: https://depositonce.tu-berlin.de/handle/11303/9981
http://dx.doi.org/10.14279/depositonce-8972
Issue Date: 23-Feb-2018
Date Available: 10-Sep-2019
DDC Class: 550 Geowissenschaften
006 Spezielle Computerverfahren
Subject(s): random forests
stacking
ensemble learning
classification
PolSAR
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: ISPRS International Journal of Geo-Information
Publisher: MDPI
Publisher Place: Basel
Volume: 7
Issue: 2
Article Number: 74
Publisher DOI: 10.3390/ijgi7020074
EISSN: 2220-9964
Appears in Collections:FG Computer Vision & Remote Sensing » Publications

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