# Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

## TL;DR

This paper introduces a Fourier-based rotation-invariant feature boosting framework for geospatial object detection, offering robustness to object deformations and improved computational efficiency in remote sensing imagery analysis.

## Contribution

The proposed FRIFB framework combines Fourier-based rotation-invariant features with aggregate channel features for faster and more robust geospatial object detection.

## Key findings

- Outperforms previous state-of-the-art methods on NWPU VHR-10 dataset subsets.
- Achieves faster feature extraction through scale factor estimation.
- Demonstrates robustness to object deformations like rotation and scaling.

## Abstract

Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods.