SAR Ship Target Recognition via Multiscale Feature Attention and Adaptive-Weighed Classifier

Wang, Chenwei; Pei, Jifang; Luo, Siyi; Huo, Weibo; Huang, Yulin; Zhang, Yin; Yang, Jianyu

doi:10.1109/lgrs.2023.3259971

Cited by 15 publications

(3 citation statements)

References 17 publications

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“…Hu et al [42] employ triplet CNNs to extract more distinctive features, with the aim of reducing the similarity between ships of the same class for enhanced classification accuracy at medium resolutions. Wang et al [43] explore the relationships among principal features to generate adaptive weights, enhancing the salient features and enlarging the feature differences between different categories, thereby improving the separability of scale-similar targets. To mitigate the impact of cross-range sidelobes on the classification performance, Zhu et al [44] leverage visual saliency models to extract ship body information, effectively eliminating sidelobe interference and resulting in improved classification performance.…”

Section: Deep Learning-based Ship Detection and Classification Method...mentioning

confidence: 99%

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

Pan,

Wu,

Dai

et al. 2024

Remote Sensing

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Synthetic aperture radar (SAR) ship detection and classification has gained unprecedented attention due to its important role in maritime transportation. Many deep learning-based detectors and classifiers have been successfully applied and achieved great progress. However, ships in SAR images present discrete and multi-centric features, and their scattering characteristics and edge information are sensitive to variations in target attitude angles (TAAs). These factors pose challenges for existing methods to obtain satisfactory results. To address these challenges, a novel target attitude angle-guided network (TAG-Net) is proposed in this article. The core idea of TAG-Net is to leverage TAA information as guidance and use an adaptive feature-level fusion strategy to dynamically learn more representative features that can handle the target imaging diversity caused by TAA. This is achieved through a TAA-aware feature modulation (TAFM) module. It uses the TAA information and foreground information as prior knowledge and establishes the relationship between the ship scattering characteristics and TAA information. This enables a reduction in the intra-class variability and highlights ship targets. Additionally, considering the different requirements of the detection and classification tasks for the scattering information, we propose a layer-wise attention-based task decoupling detection head (LATD). Unlike general deep learning methods that use shared features for both detection and classification tasks, LATD extracts multi-level features and uses layer attention to achieve feature decoupling and select the most suitable features for each task. Finally, we introduce a novel salient-enhanced feature balance module (SFB) to provide richer semantic information and capture the global context to highlight ships in complex scenes, effectively reducing the impact of background noise. A large-scale ship detection dataset (LSSDD+) is used to verify the effectiveness of TAG-Net, and our method achieves state-of-the-art performance.

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Section: Deep Learning-based Ship Detection and Classification Method...mentioning

confidence: 99%

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

Pan,

Wu,

Dai

et al. 2024

Remote Sensing

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“…Thus, Cao et al [13] proposed a ship recognition method based on morphological watershed image segmentation and Zemyk moments for ship extraction and recognition in video surveillance frame images. Wang et al [14] developed a SAR ship recognition method based on multi-scale feature attention and an adaptive weighted classifier. Zhang et al [15] presented a fine-grained ship image recognition network based on the bilinear convolutional neural network (BCNN).…”

Section: Introductionmentioning

confidence: 99%

HGR Correlation Pooling Fusion Framework for Recognition and Classification in Multimodal Remote Sensing Data

Zhang,

Huang,

Kuruoglu

2024

Remote Sensing

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This paper investigates remote sensing data recognition and classification with multimodal data fusion. Aiming at the problems of low recognition and classification accuracy and the difficulty in integrating multimodal features in existing methods, a multimodal remote sensing data recognition and classification model based on a heatmap and Hirschfeld–Gebelein–Rényi (HGR) correlation pooling fusion operation is proposed. A novel HGR correlation pooling fusion algorithm is developed by combining a feature fusion method and an HGR maximum correlation algorithm. This method enables the restoration of the original signal without changing the value of transmitted information by performing reverse operations on the sample data. This enhances feature learning for images and improves performance in specific tasks of interpretation by efficiently using multi-modal information with varying degrees of relevance. Ship recognition experiments conducted on the QXS-SROPT dataset demonstrate that the proposed method surpasses existing remote sensing data recognition methods. Furthermore, land cover classification experiments conducted on the Houston 2013 and MUUFL datasets confirm the generalizability of the proposed method. The experimental results fully validate the effectiveness and significant superiority of the proposed method in the recognition and classification of multimodal remote sensing data.

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“…With the continuous growth in underwater exploration demands, underwater target recognition [1,2,3] has become a field of increasing focus and active research in recent years. Currently, underwater detection images are mainly categorized into sonar and optical imaging methods.…”

Section: Introductionmentioning

confidence: 99%

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection

Dong,

Zhang,

Zhang

2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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SAR Ship Target Recognition via Multiscale Feature Attention and Adaptive-Weighed Classifier

Cited by 15 publications

References 17 publications

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

TAG-Net: Target Attitude Angle-Guided Network for Ship Detection and Classification in SAR Images

HGR Correlation Pooling Fusion Framework for Recognition and Classification in Multimodal Remote Sensing Data

MSSF-DCNet: multi-scale selective fusion with dense connectivity network for sonar image object detection

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