An Automatic Ship Detection Method Adapting to Different Satellites SAR Images With Feature Alignment and Compensation Loss

Zhao, Siyuan; Zhang, Zenghui; Guo, Weiwei; Luo, Ying

doi:10.1109/tgrs.2022.3160727

Cited by 26 publications

(8 citation statements)

References 44 publications

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“…Finally, the features in the two-dimensional domains are compactically fused to obtain the multi-dimensional representation of the target features. In order to better adapt to the differences brought by SAR images collected by different sensors, Zhao et al ( 2022 ) proposed an adaptive learning strategy based on the adversarial domain. Considering the different polarization modes and scattering intensity of SAR images, in order to realize the alignment of instance-level objects and pixel-level features between different domains (different sensor images), the concept of entropy is introduced as a feature weight coefficient to distinguish regions with different entropy.…”

Section: Related Workmentioning

confidence: 99%

Context-aware SAR image ship detection and recognition network

Li,

Yue,

et al. 2024

Front. Neurorobot.

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With the development of deep learning, synthetic aperture radar (SAR) ship detection and recognition based on deep learning have gained widespread application and advancement. However, there are still challenging issues, manifesting in two primary facets: firstly, the imaging mechanism of SAR results in significant noise interference, making it difficult to separate background noise from ship target features in complex backgrounds such as ports and urban areas; secondly, the heterogeneous scales of ship target features result in the susceptibility of smaller targets to information loss, rendering them elusive to detection. In this article, we propose a context-aware one-stage ship detection network that exhibits heightened sensitivity to scale variations and robust resistance to noise interference. Then we introduce a Local feature refinement module (LFRM), which utilizes multiple receptive fields of different sizes to extract local multi-scale information, followed by a two-branch channel-wise attention approach to obtain local cross-channel interactions. To minimize the effect of a complex background on the target, we design the global context aggregation module (GCAM) to enhance the feature representation of the target and suppress the interference of noise by acquiring long-range dependencies. Finally, we validate the effectiveness of our method on three publicly available SAR ship detection datasets, SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results show that our method is more competitive, with AP50s of 96.3, 93.3, and 96.2% on the three publicly available datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

Context-aware SAR image ship detection and recognition network

Li,

Yue,

et al. 2024

Front. Neurorobot.

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“…fg, when P(sal|s i ) ≥ mean P(sal|s i ) bg otherwise (15) where mean() denotes the mean intensity of the prior map in s i . Then, the observation likelihood can be computed as…”

Section: Bayesian Saliency Detection Based On the Proposed Feature En...mentioning

confidence: 99%

“…We defined P (k) c as a binary map in the kth iteration, which only retained the top ten largest connected domains in P(sal|s). The initial binary map could be obtained with (15), where the pixel value of fg 1) Update X (k+1) , Z (k+1) , β (k+1) via ( 6)-( 9)…”

Section: B Algorithm Stepsmentioning

confidence: 99%

“…As one of the key fields in SAR applications, SAR image target detection has received considerable attention [2]. Different kinds of SAR target detection methods have been proposed over the years, including constant false alarm rate (CFAR)based methods [3], [4], [5], saliency-based methods [6], [7], [8], [9], [10], [11], and deep learning-based methods [12], [13], [14], [15]. Among the above methods, saliency detection is an effective way to obtain high detection rates and low false alarm rates.…”

Section: Introductionmentioning

confidence: 99%

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Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Luo

Wang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Traditional synthetic aperture radar (SAR) target detection methods use matched filtered SAR images as input, and the detection performance is restricted due to the high sidelobes and speckle noise of these images. Sparse SAR imaging methods developed in recent years provide the advantages of reducing sidelobes, noise, and clutter. The imaging results obtained with these methods could help improve the SAR target detection performance. In this article, to improve the target detection performance using sparse SAR images as input, we proposed a convolutional sparse feature enhancement method to meet the needs of Bayesian saliency detection. The proposed Bayesian saliency joint target detection method comprised the following three steps: first, to obtain sparse SAR images with continuous contours and fewer holes in the target area, we proposed a convolutional L 1 sparse regularization method. Second, a regularization parameter optimization method was derived to quickly obtain optimal regularization parameters for saliency detection. Finally, target detection results were obtained through a superpixel-based Bayesian saliency joint detector. Extensive experiments verified that the proposed method could improve the SAR target detection accuracy in complex backgrounds.

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“…R ADAR imaging of the ship target is the important means for the maritime monitoring and ship management, and is applied widely in the military and civilian domain [1], [2], [3], [4], [5], [6]. Abundant radar imaging algorithms have been proposed to attain the high-quality radar image of ship target [7], [8], [9], [10], [11], [12], while these algorithms only concern about the short observation time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Time Selection for ISAR Imaging of Ship Target via Novel Approach of Centerline Extraction With RANSAC Algorithm

Cao

Wang

Zhang

et al. 2022

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

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Optimal time selection for inverse synthetic aperture radar imaging of ship target is significant for the determination of image project plane, and the performance of it is dependent on the accuracy of ship centerline extraction. In this article, a novel optimal time selection method based on the random sample consensus (RANSAC) technique is proposed, which can extract the ship centerline with high accuracy in the case of ship superstructure and low signal to noise ratio. First, a necessary distance threshold is determined for the RANSAC algorithm combined with the ship body width estimation. Then, a novel cost function is proposed according to the amplitude characteristic of radar image, which can alleviate the influence of randomness. Finally, the robustness of the optimal time selection method is demonstrated by the results of simulated and real measured data.

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An Automatic Ship Detection Method Adapting to Different Satellites SAR Images With Feature Alignment and Compensation Loss

Cited by 26 publications

References 44 publications

Context-aware SAR image ship detection and recognition network

Context-aware SAR image ship detection and recognition network

Saliency-Based SAR Target Detection via Convolutional Sparse Feature Enhancement and Bayesian Inference

Optimal Time Selection for ISAR Imaging of Ship Target via Novel Approach of Centerline Extraction With RANSAC Algorithm

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