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

Wu, Xin and Hong, Danfeng and Chanussot, Jocelyn and Tao, Ran and Xu, Yang and Wang, Yue (2020) 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. DOI: 10.1109/LGRS.2019.2919755 ISSN 1545-598X (In Press)

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Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8737724

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 VHR10 data set, demonstrating the superiority and effectiveness of the FRIFB compared to the previous state-of-the-art methods.

Item URL in elib:https://elib.dlr.de/128214/
Document Type:Article
Title:Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Wu, Xinaixueshuqian (at) gmail.comUNSPECIFIED
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Chanussot, Jocelyninstitute nationale polytechnique de grenobleUNSPECIFIED
Tao, RanBeijing Institute of TechnologyUNSPECIFIED
Xu, YangNanjing University of Science and TechnologyUNSPECIFIED
Wang, YueBeijing Institute of TechnologyUNSPECIFIED
Date:2020
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2019.2919755
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:In Press
Keywords:Aggregate channel features (ACFs), boosting, Fourier transformation, geospatial object detection (GOD), rotation-invariant.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Hong, Danfeng
Deposited On:05 Jul 2019 10:35
Last Modified:05 Dec 2019 11:09

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