Refocusing Swing Ships in SAR Imagery Based on Spatial-Variant Defocusing Property

Wang, Jin; Leng, Xiangguang; Sun, Zhongzhen; Zhang, Xi; Ji, Kefeng

doi:10.3390/rs15123159

Cited by 5 publications

(1 citation statement)

References 63 publications

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“…It is worth noting that this paper is mainly on ships that experience linear motion in the low sea state. However, when the ship is faced with adverse sea conditions, the complex movement of the ship makes defocusing more complex, which requires a new refocusing algorithm [53].…”

Section: Discussionmentioning

confidence: 99%

Fast and Accurate Refocusing for Moving Ships in SAR Imagery Based on FrFT

Wang,

Leng,

Sun

et al. 2023

Remote Sensing

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Synthetic aperture radar (SAR) is capable of monitoring the ocean all day, regardless of weather conditions. However, moving ships exhibit azimuth defocus in SAR images, which severely hampers ship recognition performance. Ships typically move in a linear motion at sea. For refocusing linear moving ships, existing SAR autofocus algorithms cannot accurately extract defocus information and require multiple iterations. To overcome the poor focusing quality and high computational complexity of existing refocusing algorithms, this paper proposes a fast and accurate refocusing scheme for moving ships in SAR imagery based on Fractional Fourier Transform (FrFT). Firstly, the azimuth line with the strongest energy in the SAR image is selected as the best azimuth line representing its motion property. Then, according to the entropy variation law of the azimuth line after FrFT, the azimuth line’s optimal rotation order is determined by the proposed minimum entropy search method, which can accurately and quickly obtain defocus information. In the final refocusing module, the scheme provides two ways, i.e., fast or fine refocusing approaches, to generate well-focused images. The fast refocusing approach performs FrFT on each azimuth line at the optimal rotation order of the best azimuth line. The fine refocusing approach takes the optimal rotation order of the best azimuth line as the initial value and further searches for the optimal rotation order of other azimuth lines. In order to verify the effectiveness of the proposed scheme, experiments are carried out on a number of Gaofen-3 SAR images in different acquisition modes. The experimental results show that the proposed fast refocusing approach can achieve the fastest speed, which is 2.1% of the traditional FrFT-based method’s processing time. Moreover, the proposed fine refocusing approach has the best focusing performance, achieving the lowest image entropy among existing methods.

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Section: Discussionmentioning

confidence: 99%

Fast and Accurate Refocusing for Moving Ships in SAR Imagery Based on FrFT

Wang,

Leng,

Sun

et al. 2023

Remote Sensing

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GDB‐YOLOv5s: Improved YOLO‐based model for ship detection in SAR images

Chen,

Ju,

et al. 2024

IET Image Processing

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In recent years, deep learning methods were good solutions for object detection in synthetic aperture radar (SAR) images. However, the problems of complex scenarios, large object scale differences and imperfect fine‐grained classification in ship detection were intractable. In response, an improved model GDB‐YOLOv5s (Improved YOLOv5s model incorporating global attention mechanism (GAM), DCN‐v2 and BiFusion) is designed. This model introduces deformable convolution networks (DCN‐v2) into the Backbone to enhance the adaptability of the receptive field. It replaces the original Neck's PANet structure with a BiFusion structure to better fuse the extracted multiscale features. Additionally, it integrates GAM into the network to reduce information loss and improve global feature interaction. Experiments were conducted on single‐class dataset SSDD and multi‐class dataset SRSSD‐V1.0. The results show that the GDB‐YOLOv5s model improves mean average precision scores (mAP) significantly, outperforming the original YOLOv5s model and other traditional methods. GDB‐YOLOv5s also improves Precision‐score (P) and Recall‐score (R) for fine‐grained classification to some extent, thereby reducing false alarms and missed detections. It has been proved that the improved model is relatively effective.

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