Online Random Forests For Large-Scale Land-Use Classification From Polarimetric Sar Images

Abstract

The deployment of numerous air- and space-borne remote sensing sensors as well as new data policies led to a tremendous increase of available data. While methods such as neural networks are trained by online or batch processing, i.e. keeping only parts of the data in the memory, other methods such as Random Forests require offline processing, i.e. keeping all data in the memory of the computer. The latter are therefore often trained on a small subset of a larger data set that is hoped to be representative instead of exploiting the information contained in all samples. This paper shows that Random Forests can be trained by batch processing too making their application to large data sets feasible without further constraints. The benefits of this training scheme are illustrated for the use case of land-use classification from PolSAR imagery.