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CARNet: An effective method for SAR image interference suppression
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-29 , DOI: 10.1016/j.jag.2022.103019
Shunjun Wei, Hao Zhang, Xiangfeng Zeng, Zichen Zhou, Jun Shi, Xiaoling Zhang

Synthetic aperture radar (SAR) routinely confronts the interference of radiofrequency devices in normal missions, causing ineffective imaging and seriously affecting Earth observation capability. In general, it is a great challenge to ensure interference suppression performance and image quality. To address this problem, we present an efficient method for SAR image interference suppression based on the Combined-Attention Restoration Network (CARNet). SAR image model is established, including target image, interference image, and background noise image. Specifically, we first propose a new feature extraction scheme to capture image model information over space and channels for enriching the context. Then encoder–decoder is employed to suppress interference and produce different-dimensional feature maps for target information exchange. Moreover, the image attention mechanism is introduced to calibrate the target features under the guidance of original images for essential information propagation. Besides, several attentional connections exist to prevent further loss of target details. The effectiveness of the proposed method is validated on simulated data and measured Sentinel-1 images. Compared with conventional and state-of-the-art algorithms, the results indicate that CARNet achieves better interference suppression performance and can generate high-resolution images closer to the ground truth.



中文翻译:

CARNet:一种有效的SAR图像干扰抑制方法

合成孔径雷达(SAR)在正常任务中经常面临射频设备的干扰,造成成像效果不佳,严重影响对地观测能力。总的来说,确保干扰抑制性能和图像质量是一个很大的挑战。为了解决这个问题,我们提出了一种基于联合注意力恢复网络(CARNet)的 SAR 图像干扰抑制的有效方法。建立SAR图像模型,包括目标图像、干扰图像和背景噪声图像。具体来说,我们首先提出了一种新的特征提取方案来捕获空间和通道上的图像模型信息,以丰富上下文。然后使用编码器-解码器来抑制干扰并产生不同维度的特征图用于目标信息交换。而且,引入图像注意机制,在原始图像的指导下对目标特征进行标定,以进行必要的信息传播。此外,存在几个注意连接以防止进一步丢失目标细节。在模拟数据和测量的 Sentinel-1 图像上验证了所提出方法的有效性。与传统和最先进的算法相比,结果表明 CARNet 实现了更好的干扰抑制性能,并且可以生成更接近真实情况的高分辨率图像。在模拟数据和测量的 Sentinel-1 图像上验证了所提出方法的有效性。与传统和最先进的算法相比,结果表明 CARNet 实现了更好的干扰抑制性能,并且可以生成更接近真实情况的高分辨率图像。在模拟数据和测量的 Sentinel-1 图像上验证了所提出方法的有效性。与传统和最先进的算法相比,结果表明 CARNet 实现了更好的干扰抑制性能,并且可以生成更接近真实情况的高分辨率图像。

更新日期:2022-09-29
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