  With the rapid development of spaceborne imaging techniques, object detection
in optical remote sensing imagery has drawn much attention in recent decades.
While many advanced works have been developed with powerful learning
algorithms, the incomplete feature representation still cannot meet the demand
for effectively and efficiently handling image deformations, particularly
objective scaling and rotation. To this end, we propose a novel object
detection framework, called optical remote sensing imagery detector (ORSIm
detector), integrating diverse channel features extraction, feature learning,
fast image pyramid matching, and boosting strategy. ORSIm detector adopts a
novel spatial-frequency channel feature (SFCF) by jointly considering the
rotation-invariant channel features constructed in frequency domain and the
original spatial channel features (e.g., color channel, gradient magnitude).
Subsequently, we refine SFCF using learning-based strategy in order to obtain
the high-level or semantically meaningful features. In the test phase, we
achieve a fast and coarsely-scaled channel computation by mathematically
estimating a scaling factor in the image domain. Extensive experimental results
conducted on the two different airborne datasets are performed to demonstrate
the superiority and effectiveness in comparison with previous state-of-the-art
methods.
