An Effective Method Based on ACF for Aircraft Detection in Remote Sensing Images

Zhao, An; Fu, Kun; Sun, Hao; Sun, Xian; Li, Feng; Zhang, Daobing; Wang, Hongqi

doi:10.1109/lgrs.2017.2677954

Cited by 31 publications

(18 citation statements)

References 13 publications

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“…9(a)). But this issue can be well fixed by a two-step nonmaximum suppression (NMS) algorithm [36]. The improved results can be found in Fig.…”

Section: ) Discussion On Classifier Selection: As Listed Inmentioning

confidence: 99%

“…Therefore, these benefits make the model successfully applied to pedestrian detection at over 30 fps on an 8 cores machine Inter Core i7-870 PC [43]. Similarly, it has been also proven to be effective in aircrafts detection of remote sensing images [36]. There is, however, an important assumption in the model, that is, the feature channels Ω are supposed to be any low-level shiftinvariant in order to fit the operation of sliding windows, which makes the fast detection framework sensitive to angle variation or rotation-induced deformations.…”

Section: B Feature Channel Scalingmentioning

confidence: 95%

“…Unfortunately, using orientation features alone is prone to hinder the detection performance from further improving. Inspired by the aggregate channel features (ACF) [28], Zhao et al [36] extended the channel features by additionally considering color channel features (e.g., gray-scale, RGB, HSV and LUV) to detect aircrafts through remote sensing images. However, these methods usually fail to achieve desirable performances due to the sensitivity to object rotation.…”

Section: A Channel Featuresmentioning

confidence: 99%

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

Hong

Tian

et al. 2019

IEEE Trans. Geosci. Remote Sensing

208

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This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Geoscience andRemote Sensing on IEEE Xplore. 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 stateof-the-art methods.

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“…9(a)). But this issue can be well fixed by a two-step nonmaximum suppression (NMS) algorithm [36]. The improved results can be found in Fig.…”

Section: ) Discussion On Classifier Selection: As Listed Inmentioning

confidence: 99%

Section: B Feature Channel Scalingmentioning

confidence: 95%

Section: A Channel Featuresmentioning

confidence: 99%

See 1 more Smart Citation

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

Hong

Tian

et al. 2019

IEEE Trans. Geosci. Remote Sensing

208

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show abstract

“…For those reasons, aircraft detection is present in the most advanced aerial scene recognition benchmarks [21], [22], and there is extensive literature addressing airport operation, the great majority devoted to stationary aircraft [23]- [26]. However, the number of parked airplanes is not directly correlatable with airport traffic.…”

Section: Introductionmentioning

confidence: 99%

Measuring Economic Activity From Space: A Case Study Using Flying Airplanes and COVID-19

Segundo

Pinto

Minetto

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This work introduces a novel solution to measure economic activity through remote sensing for a wide range of spatial areas. We hypothesize that disturbances in human behavior caused by major life-changing events leave signatures in satellite imagery that allows devising relevant image-based indicators to estimate their impacts and support decision-makers. We present a case study for the COVID-19 coronavirus outbreak, which imposed severe mobility restrictions and caused worldwide disruptions, using flying airplane detection around the 30 busiest airports in Europe to quantify and analyze the lockdown's effects and post-lockdown recovery. Our solution won the Rapid Action Coronavirus Earth observation (RACE) upscaling challenge, sponsored by the European Space Agency and the European Commission, and now is integrated into the RACE dashboard. This platform combines satellite data and artificial intelligence to promote a progressive and safe reopening of essential activities. Code, trained model, and data are available at https://github.com/maups/covid19-custom-script-contest.

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“…The other group of methods have handcrafted features or handcrafted architectures [13][14][15][16][17][18][19][20][21][22]. They either concentrate on better feature representation for objects [18,19], or focus on flexibly modeling the object structures and appearance [17,20], or try to design a better classifier [21,22]. This group has been extensively studied in previous decades before the emergence of deep learning-based methods.…”

Section: Introductionmentioning

confidence: 99%

Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

et al. 2018

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Partially occluded object detection (POOD) has been an important task for both civil and military applications that use high-resolution remote sensing images (HR-RSIs). This topic is very challenging due to the limited object evidence for detection. Recent partial configuration model (PCM) based methods deal with occlusion yet suffer from the problems of massive manual annotation, separate parameter learning, and low training and detection efficiency. To tackle this, a unified PCM framework (UniPCM) is proposed in this paper. The proposed UniPCM adopts a part sharing mechanism which directly shares the root and part filters of a deformable part-based model (DPM) among different partial configurations. It largely reduces the convolution overhead during both training and detection. In UniPCM, a novel DPM deformation deviation method is proposed for spatial interrelationship estimation of PCM, and a unified weights learning method is presented to simultaneously obtain the weights of elements within each partial configuration and the weights between partial configurations. Experiments on three HR-RSI datasets show that the proposed UniPCM method achieves a much higher training and detection efficiency for POOD compared with state-of-the-art PCM-based methods, while maintaining a comparable detection accuracy. UniPCM obtains a training speedup of maximal 10× and 2.5× for airplane and ship, and a detection speedup of maximal 7.2×, 4.1× and 2.5× on three test sets, respectively.

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An Effective Method Based on ACF for Aircraft Detection in Remote Sensing Images

Cited by 31 publications

References 13 publications

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

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

Measuring Economic Activity From Space: A Case Study Using Flying Airplanes and COVID-19

Unified Partial Configuration Model Framework for Fast Partially Occluded Object Detection in High-Resolution Remote Sensing Images

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