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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Meaningful Object Segmentation From SAR Images via a Multiscale Nonlocal Active Contour Model
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Meaningful Object Segmentation From SAR Images via a Multiscale Nonlocal Active Contour Model

机译：通过多尺度非局部主动轮廓模型从SAR图像中进行有意义的对象分割

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摘要

The segmentation of synthetic aperture radar (SAR) images is a long-standing yet challenging task, not only because of the presence of speckle but also due to the variations of surface backscattering properties in the images. Tremendous investigations have been made to suppress the speckle effects for the segmentation of SAR images, whereas few works are devoted to dealing with the variations of backscattering intensities in the images. To overcome the two difficulties, this paper presents a novel SAR image segmentation method by exploiting a multiscale active contour model based on the nonlocal processing principle. More precisely, we first formulize the SAR segmentation problem with an active contour model by integrating the nonlocal interactions between pairs of patches inside and outside the segmented regions. Second, a multiscale strategy is proposed to speed up the nonlocal active contour segmentation procedure and to avoid falling into a local minimum for achieving more accurate segmentation results. Experimental results on simulated and real SAR images demonstrate the efficiency and feasibility of the proposed method: It can not only achieve precise segmentations for images with heavy speckle and nonlocal intensity variations but also be used for SAR images from different types of sensors.
机译：合成孔径雷达（SAR）图像的分割是一项长期但具有挑战性的任务，这不仅是因为存在斑点，而且还因为图像中的表面后向散射特性发生了变化。为了抑制SAR图像分割中的斑点效应，已经进行了大量研究，而很少有研究致力于处理图像中反向散射强度的变化。为克服这两个难题，本文提出了一种基于非局部处理原理的多尺度主动轮廓模型，用于SAR图像分割。更准确地说，我们首先通过整合轮廓区域内部和外部区域之间成对的小块之间的非局部相互作用，用主动轮廓模型来构造SAR分割问题。其次，提出了一种多尺度策略来加速非局部主动轮廓分割过程，并避免落入局部最小值以实现更准确的分割结果。在模拟和真实SAR图像上的实验结果证明了该方法的有效性和可行性：该方法不仅可以对具有较大斑点和非局部强度变化的图像实现精确分割，而且可以用于来自不同类型传感器的SAR图像。

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