Online Random Forests for Urban Area Classification from Polarimetric SAR Images

Abstract

The growing amount of available image data renders methods unfeasible that require offline processing, i.e. the availability of all data in the memory of the computer. This paper illustrates how Random Forests can be trained by batch processing, i.e. at every iteration only a small amount of samples need to be kept in memory. The benefits of this training scheme are illustrated for the use case of urban area detection from PolSAR imagery. The achieved optimization performance is on par with using all data in the standard offline procedure.