ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

Xin Wu, Danfeng Hong*, Jiaojiao Tian, Jocelyn Chanussot, Wei Li, Ran Tao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

203 Citations (Scopus)

Abstract

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. An ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in the frequency domain and the original spatial channel features (e.g., color channel and 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 data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.

Original languageEnglish
Article number8654203
Pages (from-to)5146-5158
Number of pages13
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume57
Issue number7
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Object detection
  • optical remote sensing imagery
  • rotation invariant (RI)
  • spatial-frequency domains

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