Performance Analysis of Time-Frequency Technique for the Detection of Small Ships in SAR Imagery at Large Grazing Angle and Moderate Metocean Conditions

Velotto, Domenico; Tings, Björn

doi:10.1109/igarss.2018.8517472

Cited by 3 publications

(1 citation statement)

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“…Secondly, ships are normally quite small in SAR images. Therefore, resolutions are too small to easily locate ships [14][15][16][17]. Thirdly, for complex backgrounds (e.g., coast), ship detection is heavily affected by the scattering points from the background [6,[18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Integrating Prior Knowledge into Attention for Ship Detection in SAR Images

Pan

et al. 2023

Applied Sciences

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Although they have achieved great success in optical images, deep convolutional neural networks underperform for ship detection in SAR images because of the lack of color and textual features. In this paper, we propose our framework which integrates prior knowledge into neural networks by means of the attention mechanism. Because the background of ships is mostly water surface or coast, we use clustering algorithms to generate the prior knowledge map from brightness and density features. The prior knowledge map is later resized and fused with convolutional feature maps by the attention mechanism. Our experiments demonstrate that our framework is able to improve various one-stage and two-stage object detection algorithms (Faster R-CNN, RetinaNet, SSD, and YOLOv4) on two benchmark datasets (SSDD, LS-SSDD, and HRSID).

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