We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Classification of PolSAR Images by Stacked Random Forests.
- Authors
Hänsch, Ronny; Hellwich, Olaf
- 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.
- Subjects
RANDOM forest algorithms; IMAGE quality in synthetic aperture radar; POLARIMETRIC remote sensing
- Publication
ISPRS International Journal of Geo-Information, 2018, Vol 7, Issue 2, p74
- ISSN
2220-9964
- Publication type
Article
- DOI
10.3390/ijgi7020074