Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection

Gao, Gui; Bai, Qilin; Zhang, Chuan; Zhang, Linlin; Yao, Libo

doi:10.1016/j.isprsjprs.2023.07.006

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…In Equation (5), t is a six-dimensional vector, Ψ 3 is a 3 × 3 matrix complete basis based on Hermitian space, and k i (i = 1, 2, 3) represents the three elements in S 3DL .…”

Section: Geometrical Perturbation-polarimetric Notch Filter (Gp-pnf)mentioning

confidence: 99%

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Nearshore Ship Detection in PolSAR Images by Integrating Superpixel-Level GP-PNF and Refined Polarimetric Decomposition

Wu,

Wang,

Deng

et al. 2024

Remote Sensing

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Nearshore ship detection has significant applications in both the military and civilian domains. Compared to synthetic aperture radar (SAR), polarimetric synthetic aperture radar (PolSAR) provides richer information for analyzing the scattering mechanisms of ships and enables better detection of ship targets. However, ships in nearshore areas tend to be highly concentrated, and ship detection is often affected by adjacent strong scattering, resulting in false alarms or missed detections. While the GP-PNF detector performs well in PolSAR ship detection, it cannot obtain satisfactory results in these scenarios, and it also struggles in the presence of azimuthal ambiguity or strong clutter interference. To address these challenges, we propose a nearshore ship detection method named ECD-PNF by integrating superpixel-level GP-PNF and refined polarimetric decomposition. Firstly, polarimetric superpixel segmentation and sea–land segmentation are performed to reduce the influence of land on ship detection. To estimate the sea clutter more accurately, an automatic censoring (AC) mechanism combined with superpixels is used to select the sea clutter superpixels. By utilizing refined eight-component polarimetric decomposition to improve the scattering vector, the physical interpretability of the detector is enhanced. Additionally, the expression of polarimetric coherence is improved to enhance the target clutter ratio (TCR). Finally, this paper combines the third eigenvalue of eigenvalue–eigenvector decomposition to reduce the impact of azimuthal ambiguity. Three spaceborne PolSAR datasets from Radarsat-2 and GF-3 are adopted in the experiments for comparison. The proposed ECD-PNF method achieves the highest figure of merit (FoM) value of 0.980, 1.000, and 1.000 for three datasets, validating the effectiveness of the proposed method.

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“…In Equation (5), t is a six-dimensional vector, Ψ 3 is a 3 × 3 matrix complete basis based on Hermitian space, and k i (i = 1, 2, 3) represents the three elements in S 3DL .…”

Section: Geometrical Perturbation-polarimetric Notch Filter (Gp-pnf)mentioning

confidence: 99%

“…SAR has the advantage of all-weather and all-day monitoring, while polarimetric SAR (PolSAR) systems could capture richer polarization information compared to single-polarization SAR systems [2]. With the development of polarization theory, an increasing number of ship detection methods have been proposed for PolSAR images [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Nearshore Ship Detection in PolSAR Images by Integrating Superpixel-Level GP-PNF and Refined Polarimetric Decomposition

Wu,

Wang,

Deng

et al. 2024

Remote Sensing

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show abstract

“…The dense connection structure integrates lower-level features and higher-level features, which can introduce more context information. Multiscale fusion feature maps were used in the fully convolutional detection subnetwork in [23] to realize the fusion and comprehensive utilization of features, thereby enhancing the expression ability of multi-scale features.…”

Section: Introductionmentioning

confidence: 99%

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Wang,

Hou,

Xin

et al. 2024

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

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Synthetic aperture radar (SAR) can operate around the clock and in all weather, and therefore high-resolution SAR images have been frequently applied for ship inspection. However, current ship target detection and identification methods have limited detection accuracy and lead to missing detection of small targets due to speckle noise caused by the imaging principle of SAR imagery and complex nearshore interference. Therefore, this paper proposes an improved YOLOv5 method to address the problem of low accuracy in multi-ship target detection tasks in complex scenes. The developed scheme enhances the ship target detection performance while reducing the number of parameters. Specifically, first, we improve the size of the input SAR images and optimize the anchor frames of ship targets in the training dataset to locate small target ships more accurately. Then, symmetric pyramidal non-local block(APNB) and Sim attention mechanism (SimAM) are introduced to reduce near-shore background interference. Additionally, to make the C3 module output richer and with more feature information, channel shuffling is performed after the C3 output to enhance the information exchange between channels. Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the NECK part is replaced with Ghost convolution. The index F1 of the proposed method on the High-Resolution SAR Image Dataset (HRSID) and SAR Ship Detection Dataset (SSDD) reaches the highest of 91.3% and 95.8%, respectively. MAPs (0.5:0.95) for the two datasets are both the highest, which are at least 2% higher than the suboptimal method. In selected specific inshore scenes, the ship detection performance of the proposed method outperforms current advanced methods for multiscale ships. It is shown that the proposed method can extract ship features effectively in complex scenes and its effectiveness is further validated on the large scene AIR-SARShip-1 dataset.

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Polarimetric calibration of linear dual-pol SAR when corner reflectors are unavailable

Shi,

Sun,

Yang

et al. 2024

ISPRS Journal of Photogrammetry and Remote Sensing

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Dualistic cascade convolutional neural network dedicated to fully PolSAR image ship detection

Cited by 10 publications

References 28 publications

Nearshore Ship Detection in PolSAR Images by Integrating Superpixel-Level GP-PNF and Refined Polarimetric Decomposition

Nearshore Ship Detection in PolSAR Images by Integrating Superpixel-Level GP-PNF and Refined Polarimetric Decomposition

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Polarimetric calibration of linear dual-pol SAR when corner reflectors are unavailable

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