Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, Epiciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Abstract : Geospatial object detection (GOD) of remote sensing imagery has been attracting increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of the 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 GOD 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 the image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 data set, demonstrating the superiority and effectiveness of the FRIFB compared to the previous state-of-the-art methods.
Document type :
Journal articles
Complete list of metadata
Contributor : Vincent Couturier-Doux Connect in order to contact the contributor
Submitted on : Monday, October 7, 2019 - 4:26:55 PM
Last modification on : Wednesday, November 3, 2021 - 6:01:39 AM

Links full text




Xin Wu, Danfeng Hong, Jocelyn Chanussot, yang Xu, Ran Tao, et al.. Fourier-Based Rotation-Invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection. IEEE Geoscience and Remote Sensing Letters, IEEE - Institute of Electrical and Electronics Engineers, 2020, 17 (2), pp.302-306. ⟨10.1109/LGRS.2019.2919755⟩. ⟨hal-02307442v2⟩



Record views