Research on Aircraft Object Recognition Model Based on Neural Networks

Henan, Wu; Li, Dejun; Wang, Hongwei; Liu, Ying; Sun, Xiaorui

doi:10.1109/iccsee.2012.330

Cited by 5 publications

(2 citation statements)

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“…In recent years, researchers have also made some efforts to identify different types of airplane. Some researchers directly classify airplanes with deep neural networks (DNNs) [20]- [23]. Fang et al [20] introduced the Backward Propagation (BP) neural network to extract image features and recognize the airplanes in RSIs.…”

Section: B Airplane Detection In Rsismentioning

confidence: 99%

Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images

Jia

Guo

Chen

et al. 2021

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

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Airplane detection and recognition in high-resolution Remote Sensing Images (RSIs) remains a challenging task due to the factors of multiple view angles, multiple scales, multiple orientation etc. This paper proposes an Adaptive Component Discrimination Network (ACDN) for airplane detection and recognition in RSIs, which focus various scales from global to local, making full use of the overall contour as well as the dominant component features of airplanes. Firstly, a Standardization Processing Module (SPM) is proposed for image projection conversion and resolution uniform to alleviate the confusion of different types of airplane in different resolution images. Secondly, the Rotatable Bounding box-based Pyramid Network (RBPN) is utilized to extract candidate airplane coordinates and categories. Further, an adaptive aircraft component discrimination method is established for confusing few-shot airplane targets recognition, which consists of a Target Orientation Adaptive Adjustment Module (OAAM) and a Component Discrimination Module (CDM). OAAM obtains airplanes with same orientations by predicting the orientation of the slices and rotating them adaptively. All the uniformed slices are then fed into the CDM for dominant components detection, which corrects the target preclassification results, improving the classification performance. Experiments conducted on the 2020 Gaofen Challenge demonstrate the efficacy and superiority of the proposed method.

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Section: B Airplane Detection In Rsismentioning

confidence: 99%

Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images

Jia

Guo

Chen

et al. 2021

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

View full text Add to dashboard Cite

show abstract

“…Fang et al [5] adopted a back propagation neural network and a series of preprocessing methods to deal with this problem. Additionally, multi-layer perception [6] and the deep belief net (DBN) [7] have been applied to recognition tasks. However, the networks used in the aforementioned methods are not deep enough to learn robust features for recognizing various aircrafts in complex backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

et al. 2018

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Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts' keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

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Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

Zeng

Ming

et al. 2022

International Journal of Remote Sensing

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Research on Aircraft Object Recognition Model Based on Neural Networks

Cited by 5 publications

References 0 publications

Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images

Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images

Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

Top-Down aircraft detection in large-scale scenes based on multi-source data and FEF-R-CNN

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