Classification of PolSAR Images by Stacked Random Forests

ISPRS International Journal of Geo-Information

Ronny Hänsch¹  Olaf Hellwich¹ 

[1] Computer Vision & Remote Sensing, Technische Universität Berlin, Berlin 10587, Germany;

关键词: random forests;    stacking;    ensemble learning;    classification;    PolSAR;   

DOI  :  10.3390/ijgi7020074

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown