  As an important research topic in computer vision, fine-grained
classification which aims to recognition subordinate-level categories has
attracted significant attention. We propose a novel region based ensemble
learning network for fine-grained classification. Our approach contains a
detection module and a module for classification. The detection module is based
on the faster R-CNN framework to locate the semantic regions of the object. The
classification module using an ensemble learning method, which trains a set of
sub-classifiers for different semantic regions and combines them together to
get a stronger classifier. In the evaluation, we implement experiments on the
CUB-2011 dataset and the result of experiments proves our method s efficient
for fine-grained classification. We also extend our approach to remote scene
recognition and evaluate it on the NWPU-RESISC45 dataset.
