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首页> 外文期刊>Journal of visual communication & image representation >Object detection in SAR image based on bandlet transform
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Object detection in SAR image based on bandlet transform

机译：基于小波变换的SAR图像目标检测

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摘要

detection in SAR images is a challenging task as these images are inherently affected with speckle noise. This paper presents a novel algorithm based on bandlet transform for object detection in Synthetic Aperture Radar (SAR) images. Here first a bandlet based despeckling scheme is employed on the input SAR image and then a constant false alarm rate (CFAR) detector is used for object detection. The input image is first decomposed using Bandlet transform and the bandlet coefficients so obtained are modified using soft thresholding rule on all sub bands, except for low frequency sub band. The optimum thresholds for each sub bands are computed using generalized cross-validation (GCV) technique which doesn't require the information on noise variance of the input image. The method takes advantage of the geometrical features of bandlet transform for retaining the edges and boundaries of the objects, present in SAR images while removing the speckle effectively. Thus CFAR detection on despeckled image can effectively find an optimum threshold for object detection to maintain a constant false alarm rate. The proposed Bandlet transform based scheme surpasses the traditional despeckling and object detection schemes in wavelet domain, in terms of numerical and visual quality. (C) 2016 Elsevier Inc. All rights reserved.
机译：在SAR图像中进行检测是一项艰巨的任务，因为这些图像固有地受到斑点噪声的影响。本文提出了一种基于小波变换的合成孔径雷达（SAR）图像目标检测算法。在这里，首先在输入SAR图像上采用基于带的去斑点方案，然后将恒定误报率（CFAR）检测器用于目标检测。首先使用Bandlet变换分解输入图像，并使用除所有低频子带之外的所有子带上的软阈值规则修改由此获得的子带系数。每个子带的最佳阈值使用通用交叉验证（GCV）技术计算得出，该技术不需要有关输入图像噪声方差的信息。该方法利用小波变换的几何特征来保留SAR图像中存在的对象的边缘和边界，同时有效去除斑点。因此，对散斑图像的CFAR检测可以有效地找到用于物体检测的最佳阈值，以保持恒定的误报率。提出的基于Bandlet变换的方案在数值和视觉质量方面都超过了小波域中的传统去斑点和目标检测方案。 （C）2016 Elsevier Inc.保留所有权利。

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