FASTER TREES: STRATEGIES FOR ACCELERATED TRAINING AND PREDICTION OF RANDOM FORESTS FOR CLASSIFICATION OF POLSAR IMAGES

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Ronny Hänsch^1¹ 

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

关键词: Random Forest;    PolSAR;    Information Extraction;    Classification;    Computation Time;   

DOI  :  10.5194/isprs-annals-IV-3-105-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Random Forests have continuously proven to be one of the most accurate, robust, as well as efficient methods for the supervised classification of images in general and polarimetric synthetic aperture radar data in particular. While the majority of previous work focus on improving classification accuracy, we aim for accelerating the training of the classifier as well as its usage during prediction while maintaining its accuracy. Unlike other approaches we mainly consider algorithmic changes to stay as much as possible independent of platform and programming language. The final model achieves an approximately 60 times faster training and a 500 times faster prediction, while the accuracy is only marginally decreased by roughly 1 %.

【 授权许可】

CC BY