Joint Detection of Airplane Targets Based on Sar Images and Optical Images

Qin, Jitao; Qu, Haicheng; Chen, Hao; Chen, Wen

doi:10.1109/igarss.2019.8900167

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Therefore, detection methods usually use one-class classifiers to this situation [57,58]. Currently, one-class support vector machine (OCSVM) is very popular in the field of radar detection [59][60][61][62], so we choose OCSVM to determine whether a target exists.…”

Section: Classification Modulementioning

confidence: 99%

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

et al. 2023

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In ground static target detection, polarimetric high-resolution radar can distinguish the target from the strong ground clutter by reducing the clutter power in the range cell and providing additional polarimetric features. Since the energy of a target is split over several range cells, the resulting detection problem is called polarimetric range extended target (RET) detection, where all target scattering centers should be considered. In this paper, we propose a novel polarimetric RET detection method via adaptive range weighted feature extraction. Specifically, polarimetric features of range cells are extracted, and a pretrained attention-mechanism-based module is used to adaptively calculate range cells weights, which are used to accumulate the range cells features as detection statistics. While calculating weights, both amplitude and polarimetric features are considered. This method can make the most of polarization information and improve the accumulation effect, thus increasing the discrimination between targets and clutter. The effectiveness of the proposed method is verified compared to both popular energy-domain detection methods and existing feature-domain detection methods, and the results show that our method exhibits superior detection performance. Moreover, we further analyze our method on different target models and different clutter distributions to prove that our method is suitable for different types of targets and clutter.

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Section: Classification Modulementioning

confidence: 99%

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

et al. 2023

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Multi-Path Interactive Network for Aircraft Identification with Optical and SAR Images

et al. 2022

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Aircraft identification has been a research hotspot in remote-sensing fields. However, due to the presence of clouds in satellite-borne optical imagery, it is difficult to identify aircraft using a single optical image. In this paper, a Multi-path Interactive Network (MIN) is proposed to fuse Optical and Synthetic Aperture Radar (SAR) images for aircraft identification on cloudy days. First, features are extracted from optical and SAR images separately by convolution backbones of ResNet-34. Second, a piecewise residual fusion strategy is proposed to reduce the effect of clouds. A plug-and-play Interactive Attention Sum-Max fusion module (IASM), is thus constructed to interact with features from multi-modal images. Moreover, multi-path IASM is designed to mix multi-modal features from backbones. Finally, the fused features are sent to the neck and head of MIN for regression and classification. Extensive experiments are carried out on the Fused Cloudy Aircraft Detection (FCAD) dataset that is constructed, and the results show the efficiency of MIN in identifying aircraft under clouds with different thicknesses.Compared with the single-source model, the multi-source fusion model MIN is improved by more than 20%, and the proposed method outperforms the state-of-the-art approaches.

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Joint Detection of Airplane Targets Based on Sar Images and Optical Images

Cited by 2 publications

References 7 publications

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

Polarimetric Range Extended Target Detection via Adaptive Range Weighted Feature Extraction

Multi-Path Interactive Network for Aircraft Identification with Optical and SAR Images

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