基于超像素级条件三场的SAR图像快速分割算法研究
发布时间：2019-06-05 11:50
【摘要】：合成孔径雷达(Synthetic aperture radar,SAR)是一种高分辨率的微波成像雷达,具有全天时、全天候工作,有效地识别伪装等优势,已广泛应用于农业、军事和海洋等领域,有着广阔的应用前景和发展潜力。SAR图像中通常包含有多种地物目标的信息,如何有效的对图像中各类目标进行准确分割,对SAR图像的解译具有重要意义。SAR图像分割是SAR图像解译的重要组成部分,也是当前SAR遥感领域研究的热点与难点。由于SAR的成像机理决定了SAR图像中不可避免的引入大量乘性斑点噪声,基于光学图像的分割方法在SAR图像上很难取得良好结果。近年来,随机场模型理论的发展,为SAR图像的分割开辟了一条新的路径。本文就如何获得有效和高效的SAR分割结果做了研究,提出了基于超像素级条件三场(Superpixel-level conditional triplet Markov field,SL-CTMF)的SAR图像快速分割方法,主要的工作和贡献如下:1.条件随机场(Conditional random field,CRF)可以直接对图像后验进行建模,但对SAR图像的建模缺少有效的训练数据和训练机制,所以CRF在SAR图像分割上的应用受到限制。三重马尔可夫随机场(Triplet Markov random field,TMF)引入了辅助U场来有效描述SAR图像的非平稳性,较好抑制了乘性斑点噪声对SAR图像分割所带来的影响,取得了良好的分割结果,但TMF建模复杂,并且不能充分利用观测数据的相关性。2.CRF直接对后验概率进行建模的思想,正好解决了TMF模型存在的缺点,并因此产生了像素级条件三场(Pixel-level conditional triplet Markov field,PL-CTMF)模型。该模型充分结合了CRF和TMF的优势:直接对X场的后验概率进行建模、并通过U场的引入来描述图像的非平稳性,简化了SAR图像的建模方法,提高了SAR图像的分割效果。3.在有效和高效的SAR分割上,PL-CTMF模型中不管像素的特征与其周围邻域点的特征有多么相似,它依然需要计算每一个点的分类概率,低效率和高冗余是不可避免的,所以本文就提出了SL-CTMF模型用于SAR图像的快速分割。首先,针对SAR图像,我们对TurboPixels算法进行了改进,使它能够获取一个边缘定位准确的超像素SAR图像;在超像素级的SAR图像上,重新构建了辅助场U来描述SAR图像的非平稳性,SL-CTMF的一元和二元势能通过超像素级的特征和纹理信息得以重建。由于SL-CTMF正是对超像素进行标记的,且每个超像素的特征都是超像素内所有像素点特征的综合特征,所以算法的效率和分割结果的区域一致性都会得到有效提升。最后,结合最大后验边缘(Maximum posterior marginal,MPM)方法将SL-CTMF应用于无监督SAR图像的快速分割,在分割效果与PL-CTMF类似或略微好一些的前提下,SL-CTMF极大的缩短了算法的运行时间,达到了PL-CTMF的1/4到1/6。
[Abstract]:Synthetic Aperture Radar (Synthetic aperture radar,SAR) is a kind of high resolution microwave imaging radar, which has the advantages of all-day, all-weather work, effective identification of camouflage and so on. It has been widely used in agriculture, military and marine fields. SAR images usually contain a variety of ground object information, how to effectively segment all kinds of objects in the image. Sar image segmentation is an important part of SAR image interpretation, and it is also a hot and difficult point in the field of SAR remote sensing. Because the imaging mechanism of SAR determines that a large number of multiplicative speckle noise is inevitably introduced into SAR images, it is difficult for optical image segmentation methods to achieve good results on SAR images. In recent years, the development of random field model theory has opened up a new path for SAR image segmentation. In this paper, how to obtain effective and efficient SAR segmentation results is studied, and a fast SAR image segmentation method based on hyperpixel level conditional three fields (Superpixel-level conditional triplet Markov field,SL-CTMF) is proposed. The main work and contributions are as follows: 1. Conditional random field (Conditional random field,CRF) can directly model the posterior image, but the modeling of SAR image lacks effective training data and training mechanism, so the application of CRF in SAR image segmentation is limited. Triple Markov random field (Triplet Markov random field,TMF) introduces auxiliary U field to effectively describe the nonstationarity of SAR images, which can effectively suppress the influence of multiplicative speckle noise on SAR image segmentation, and good segmentation results are obtained. However, TMF modeling is complex and can not make full use of the correlation of observation data. 2. The idea of TMF modeling posterior probability directly solves the shortcomings of CRF model. Therefore, the pixel-level conditional three-field (Pixel-level conditional triplet Markov field,PL-CTMF) model is generated. The model fully combines the advantages of CRF and TMF: modeling the posterior probability of X field directly, and describing the nonstationarity of the image through the introduction of U field, simplifying the modeling method of SAR image and improving the segmentation effect of SAR image. 3. In the efficient and efficient SAR segmentation, no matter how similar the pixel features are to the adjacent points in the PL-CTMF model, it still needs to calculate the classification probability of each point, and low efficiency and high redundancy are inevitable. Therefore, this paper proposes a SL-CTMF model for fast segmentation of SAR images. Firstly, for SAR images, we improve the TurboPixels algorithm so that it can obtain a super-pixel SAR image with accurate edge location. On the hyperpixel level SAR image, the auxiliary field U is rebuilt to describe the nonstationarity of the SAR image. The univariate and binary potentials of SL-CTMF can be reconstructed by the hyperpixel feature and texture information. Because SL-CTMF marks the super pixel, and the feature of each super pixel is the comprehensive feature of all the pixel features in the super pixel, the efficiency of the algorithm and the regional consistency of the segmentation results will be effectively improved. Finally, combined with the maximum posterior edge (Maximum posterior marginal,MPM) method, the SL-CTMF is applied to the fast segmentation of unsupervised SAR images, and the segmentation effect is similar to or slightly better than that of PL-CTMF. SL-CTMF greatly shortens the running time of the algorithm, reaching 1 鈮，
本文编号：2493507
[Abstract]:Synthetic Aperture Radar (Synthetic aperture radar,SAR) is a kind of high resolution microwave imaging radar, which has the advantages of all-day, all-weather work, effective identification of camouflage and so on. It has been widely used in agriculture, military and marine fields. SAR images usually contain a variety of ground object information, how to effectively segment all kinds of objects in the image. Sar image segmentation is an important part of SAR image interpretation, and it is also a hot and difficult point in the field of SAR remote sensing. Because the imaging mechanism of SAR determines that a large number of multiplicative speckle noise is inevitably introduced into SAR images, it is difficult for optical image segmentation methods to achieve good results on SAR images. In recent years, the development of random field model theory has opened up a new path for SAR image segmentation. In this paper, how to obtain effective and efficient SAR segmentation results is studied, and a fast SAR image segmentation method based on hyperpixel level conditional three fields (Superpixel-level conditional triplet Markov field,SL-CTMF) is proposed. The main work and contributions are as follows: 1. Conditional random field (Conditional random field,CRF) can directly model the posterior image, but the modeling of SAR image lacks effective training data and training mechanism, so the application of CRF in SAR image segmentation is limited. Triple Markov random field (Triplet Markov random field,TMF) introduces auxiliary U field to effectively describe the nonstationarity of SAR images, which can effectively suppress the influence of multiplicative speckle noise on SAR image segmentation, and good segmentation results are obtained. However, TMF modeling is complex and can not make full use of the correlation of observation data. 2. The idea of TMF modeling posterior probability directly solves the shortcomings of CRF model. Therefore, the pixel-level conditional three-field (Pixel-level conditional triplet Markov field,PL-CTMF) model is generated. The model fully combines the advantages of CRF and TMF: modeling the posterior probability of X field directly, and describing the nonstationarity of the image through the introduction of U field, simplifying the modeling method of SAR image and improving the segmentation effect of SAR image. 3. In the efficient and efficient SAR segmentation, no matter how similar the pixel features are to the adjacent points in the PL-CTMF model, it still needs to calculate the classification probability of each point, and low efficiency and high redundancy are inevitable. Therefore, this paper proposes a SL-CTMF model for fast segmentation of SAR images. Firstly, for SAR images, we improve the TurboPixels algorithm so that it can obtain a super-pixel SAR image with accurate edge location. On the hyperpixel level SAR image, the auxiliary field U is rebuilt to describe the nonstationarity of the SAR image. The univariate and binary potentials of SL-CTMF can be reconstructed by the hyperpixel feature and texture information. Because SL-CTMF marks the super pixel, and the feature of each super pixel is the comprehensive feature of all the pixel features in the super pixel, the efficiency of the algorithm and the regional consistency of the segmentation results will be effectively improved. Finally, combined with the maximum posterior edge (Maximum posterior marginal,MPM) method, the SL-CTMF is applied to the fast segmentation of unsupervised SAR images, and the segmentation effect is similar to or slightly better than that of PL-CTMF. SL-CTMF greatly shortens the running time of the algorithm, reaching 1 鈮，
本文编号：2493507
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