Fast segmentation approach for SAR image based on simple Markov random field

Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.

Geometric active contour based approach for segmentation of high-resolution spaceborne SAR images

Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar（SAR） images. A novel method is proposed based on integrating the geometric active contour（GAC） and the support vector machine（SVM）models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items： the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.

ADAPTIVE RIVER SEGMENTATION IN SAR IMAGES

There is difficulty for distinguishing of river and shadow in Synthetic Aperture Radar (SAR) images. A method of river segmentation in SAR images based on wavelet energy and gradient is proposed in this paper. It mainly includes two algorithms: coarse segmentation and refined segmen- tation. Firstly, The river regions are coarsely segmented by the wavelet energy feature,and then refined segmented accurately by the gradient threshold which is got adaptively. The experimental results show the validity of the method, which provides a good foundation for targets detection above the river.

A fast,edge-preserving,distance-regularized model with bilateral filtering for oil spill segmentation of SAR images

Marine oil spills are among the most significant sources of marine pollution.Synthetic aperture radar(SAR)has been used to improve oil spill observations because of its advantages in oil spill detection and identification.However,speckle noise,weak boundaries,and intensity inhomogeneity often exist in the oil spill regions of SAR imagery,which will seriously aff ect the accurate identification of oil spills.To enhance marine oil spill segmentation of SAR images,a fast,edge-preserving framework based on the distance-regularized level set evolution(DRLSE)model was proposed.Specifically,a bilateral filter penalty term is designed and incorporated into the DRLSE energy function(BF-DRLSE)to preserve the edges of oil spills,and an adaptive initial box boundary was selected for the DRLSE model to reduce the operation time complexity.Two sets of RadarSat-2 SAR data were used to test the proposed method.The experimental results indicate that the bilateral filtering scheme incorporated into the energy function during level set evolution improved the stability of level set evolution.Compared with other methods,the proposed improved BF-DRLSE algorithm displayed a higher overall segmentation accuracy(97.83%).In addition,using an appropriate initial box boundary for the DRLSE method accelerated the global search process,improved the accuracy of oil spill segmentation,and reduced computational time.Therefore,the results suggest that the proposed framework is eff ective and applicable for marine oil spill segmentation.

MULTI-REGION SEGMENTATION OF SAR IMAGE BY A MULTIPHASE LEVEL SET APPROACH

In this letter,a multiphase level set approach unifying region and boundary-based infor-mation for multi-region segmentation of Synthetic Aperture Radar(SAR)image is presented.Anenergy functional that is applicable for SAR image segmentation is defined.It consists of two termsdescribing the local statistic characteristics and the gradient characteristics of SAR image respectively.A multiphase level set model that explicitly describes the different regions in one image is proposed.The purpose of such a multiphase model is not only to simplify the way of denoting multi-region by levelset but also to guarantee the accuracy of segmentation.According to the presented multiphase model,the curve evolution equations with respect to edge curves are deduced.The multi-region segmentationis implemented by the numeric solution of the partial differential equations.The performance of theapproach is verified by both simulation and real SAR images.The experiments show that the proposedalgorithm reduces the speckle effect on segmentation and increases the boundary alignment accuracy,thus correctly divides the multi-region SAR image into different homogenous regions.

Coherence-coefficient-based Markov random field approach for building segmentation from high-resolution SAR images

Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.

Reassembly of the axon initial segment and nodes of Ranvier in regenerated axons of the central nervous system

Myelinated axons of the peripheral and central nervous system（PNS＆CNS）are divided into molecularly distinct excitable domains,including the axon initial segment（AIS）and nodes of Ranvier.The AIS is composed of a dense network of cytoskeletal proteins,cell adhesion molecules,and voltage gated ion channels and is located at the proximal most region of the axon（Koleand Stuart, 2012）.

应用UAVSAR数据及改进极化水云模型对热带森林地上生物量反演

针对热带森林地上生物量遥感估测中容易饱和区域大尺度森林地上生物量估测精度低的问题,以非洲加蓬中部的洛佩达国家公园为研究区,以NASA提供的L波段全极化机载合成孔径雷达(SAR)数据和LiDAR网格化森林地上生物量产品为数据源开展森林生物量估测方法研究。采用极化分解方法提取森林的多种散射机制,从中选择反映森林结构差异的地面散射特征和森林体散射的特征构建体-地散射比,采用极化水云模型(PWCM)进行森林地上生物量反演和精度评价。为了提高PWCM模型的适应性,建模过程中根据体散射分量(V_(ol))分段进行模型的参数优化。结果表明:以Freeman三分量极化分解后得到的体散射(V_(ol))、表面散射(O_(dd))、地-干散射(D_(bl))为基础构建的6个体-地散射比在极化水云模型估算森林地上生物量中,以μ_(VG2)作为体-地散射比时估测效果最好,模型决定系数(R^(2))为0.60,均方根误差(R_(MSE))为127.78 Mg/hm^(2);在此基础上,进一步根据体散射分量分段优化极化水云模型,模型决定系数(R^(2))增加到0.74,均方根误差(R_(MSE))降低了约20%,预测精度从50.76%提升至60.28%,并改善了低值高估、高值低估问题,在地上生物量高达450 Mg/hm^(2)时未出现饱和现象。

改进三维最大类间方差的SAR图像海陆分割算法

合成孔径雷达(Synthetic Aperture Radar,SAR)是一种在气象条件较差的条件下工作的高分辨率成像雷达。无论在民用还是军事领域,用合成孔径雷达进行海上舰船图像目标检测都能起到关键作用。海陆分割作为一种SAR图像预处理的手段,可以为后续的目标检测、识别与跟踪技术研究奠定良好的基础。本文对包含较复杂岛岸背景的SAR图像的海陆分割算法进行研究,在传统三维最大类间方差算法的基础上提出一种改进的SAR图像海陆分割算法,将原有算法第三维度邻域中值改为Prewitt算子的梯度运算,Prewitt算子适合处理灰度渐变复杂的图像,SAR图像能够更好地进行图像分割。考虑到维度上增加一维会使得算法效率降低,运用分解的思想,将三维最大类间方差拆分成3个一维最大类间方差算法。通过两组SAR图像进行实验,结果表明本文提出的改进三维最大类间方差算法具有可行性,并且在海陆分割效果方面优于一维、二维最大类间方差算法,在运算复杂度方面优于传统的三维最大类间方差算法。

一种应用于物联网的高精度SAR ADC

基于180 nm CMOS工艺,设计了一种应用于物联网的14-bit逐次逼近型模数转换器(Successive Approximation Analog-to-Digital Converter,SAR ADC)。电容阵列采用分段和冗余技术,高段电容加入了两位冗余电容,在提高ADC的精度与线性度的同时也减少了版图面积。为了实现高精度,采用了一种基于电荷泵的失调电压降低技术的动态比较器,基于电荷泵的逐次逼近比较环路改变全动态预放大器两个输入晶体管的衬底电压差值,有效的补偿了失调电压,最终稳定在一个小的失调步长内。相比于传统静态预放大器,全动态预放大器节省了更多的功耗,相比于现有电荷泵补偿技术,使用更加简单的校准逻辑,大大减少数字电路的开销。动态器件匹配(DEM)技术用于提高电容阵列最高3位的电容的匹配度,将最高3位的电容拆分为大小相等的7个电容,让电容转换过程中,被选中的概率相同,将电容失配的误差平均化,从而将谐波平均分布到频域范围,以减少电容失配的影响。仿真结果表明,在采样频率为4 kS/s时,供电电压为1.8 V的条件下,无杂散动态范围为94.9 dB,功耗为1.002μW,有效位数为13.01 bit。

利用SAM大模型对SAR影像配准的深度学习方法

在SAR影像的配准中,由于SAR影像固有的斑点噪声,导致配准稳定性差、配准效率低。针对这个问题,文章提出了利用SAM大模型对SAR影像配准的深度学习方法。该方法使用SAM分割大模型对参考图像进行分割,得到参考图像的掩码,然后利用基于编码器和解码器的深度学习网络寻找该掩码在主图像中的位置,以此来寻找参考图像和主图像之间的对应关系,并把这个对应关系转成点与点的对应关系,进而达到配准的目的。实验结果表明:该方法能准确找到参考图像的掩码在主图像中的位置,均值误差在5%以内;在大尺寸SAR影像配准中,该方法具有很好的稳定性,且配准效率较高。

基于掩模注意型交互的SAR舰船实例分割

现有合成孔径雷达(synthetic aperture radar,SAR)舰船实例分割方法未实现掩模交互或交互性能有限,导致检测精度较低。针对上述问题,提出了一种基于掩模注意型交互(mask attention interaction,MAI)的SAR舰船实例分割方法MAI-Net。首先,MAI-Net使用了膨胀空间金字塔池化,来获取多分辨率特征响应,增强了对背景的鉴别能力。其次,MAI-Net使用了非局部注意力模块来抑制低价值信息,实现了空间特征自注意。最后,MAI-Net提出了拼接混洗注意力模块来平衡不同特征图的贡献,进一步提高了实例分割精度。在公开的像素级多边形分割SAR舰船检测数据集(polygon segmentation SAR ship detection dataset,PSeg-SSDD)上的实验结果表明,MAI-Net的SAR舰船实例分割精度高于现有其他11种对比模型,实例分割精度达到61.1%,高于次优模型1.5%。

基于L_(1)范数混合主动轮廓的河流SAR图像分割

为解决现有主动轮廓模型难以准确分割河流SAR图像的问题,提出一种基于L_(1)范数的混合主动轮廓模型。首先,计算轮廓曲线内外区域像素灰度的中值作为区域拟合中心,以抑制SAR图像中干扰区域对其准确性的影响;然后,利用L_(1)范数构建新的能量约束项并在模型能量泛函中引入边缘指示函数,进一步提升模型的分割性能;最后,将基于L_(1)范数的中值和均值能量约束项结合起来并添加额外的区域拟合中心约束项,以提高模型的整体稳定性。针对实际河流SAR图像进行分割试验,结果表明,与现有分割方法相比,本文模型能更准确、稳定地分割河流SAR图像。

一种低秩稀疏矩阵分解联合目标轨迹区域提取的ViSAR-GMTI方法

视频合成孔径雷达(ViSAR)中,由于观测角度的不同会引起物体后向散射系数变化从而产生动态背景,不利于复杂场景下的运动目标检测,故提出一种基于低秩稀疏矩阵分解和目标运动轨迹区域提取的ViSAR运动目标检测方法。首先,考虑目标的空间连续性以及复杂场景下的诸多干扰因素,对常规RPCA模型做出改进,引入结构稀疏范数和动态背景鲁棒项以完善分解模型,提升分解效果。然后,改进局部自适应阈值设定,使用复合聚类分割方式提取运动轨迹区域,进一步消除干扰,并在分解所得前景图像的轨迹区域上进行均值背景建模完成运动目标检测。最后,基于齐鲁一号的聚束数据进行实验,结果证明所提方法的有效性,并通过对比实验验证该方法的检测性能。

基于加权混合SPF主动轮廓的SAR图像水陆分割

针对现有主动轮廓模型(Active Contour Model,ACM)无法进行准确高效的SAR(Synthetic Aperture Radar)图像水陆分割的难题,提出一种基于加权混合符号压力(Signed Pressure Force,SPF)主动轮廓的新模型.该模型将中值拟合中心和均值拟合中心结合起来,建立新的混合符号压力函数,以抑制干扰边缘的影响.通过设计基于绝对中位差的自适应权重,实现自动调节内外拟合中心的比例系数,以更合理地描述灰度差异.此外,计算平滑后图像梯度信息构建边缘引导函数并引入到SPF函数中,以进一步提高分割性能和演化效率.在实际SAR图像上进行分割实验,结果表明:与相关实验模型相比,提出模型在保持高效的同时能获得更加准确的分割结果,其中Dice相似系数(Dice Similarity Coefficient,DSC)至少提高6%.

基于连续波SAR成像模型的输电线路悬垂绝缘子风偏角识别方法

为了在恶劣环境中保证输电线路悬垂绝缘子风偏角识别结果可信,提出基于连续波合成孔径雷达(SAR)成像模型的输电线路悬垂绝缘子风偏角识别方法。基于连续波SAR成像模型,持续发射脉冲信号,通过基于位置标定的绝缘子风偏角识别方法,定位绝缘子目标区域中绝缘子串2个端点的像素坐标,生成绝缘子SAR图像。采用基于改进频率变标算法的连续波SAR成像处理方法,缓解绝缘子SAR图像失焦现象,进行SAR图像分割,获取生成绝缘子SAR图像中绝缘子目标区域,并结合“两点确定一条直线”标准识别绝缘子串的风偏角。实验验证:所提方法在恶劣环境中像素拟合效果较佳,对绝缘子风偏角识别结果和实际风偏角的风偏度信息高度匹配。

基于海岸线分割的大尺度复杂场景SAR图像舰船检测方法

机载和星载平台下的传统海岸线分割方法大多依赖人为构造特征或使用形态学手段,往往很难平衡模型泛化能力与分割精度的关系。对此,研究机载和星载平台下的超高分辨率合成孔径雷达(Synthetic Aperture Radar,SAR)成像及其在船舶检测的应用,提出一种基于超像素分割和深度卷积神经网络的海陆分割方法,用深度特征代替人工特征并充分利用SAR图像的纹理、灰度和亮度等特征,在保证模型泛化能力的基础上具有较高的分割精度,为机载和星载平台下的舰船检测任务打下了良好的基础。

一种基于分段冗余电容阵列的高速SAR ADC

高速中等精度的模数转换器是通信系统中重要的组成部分。本文提出了一种基于分段冗余电容阵列的高速逐次逼近型模数转换器(SAR ADC)设计方案。该设计方案通过引入分段冗余电容阵列,在降低了面积和功耗的同时,克服了高速采样下,DAC不完全建立对ADC性能的影响。所设计的两级动态比较器,通过噪声分析可知,在满足高速性能的前提下,提高了ADC的精度。基于SMIC55nm CMOS工艺,本文实现了一种12-bit 100-MS/s的SAR ADC。在1.2V电源电压和100MS/s的采样频率,差分输入接近满摆幅下,前仿真结果为SNDR为73.27dB,ENOB可达11.87bit。

典型Otsu算法阈值比较及其SAR图像水域分割性能分析

遥感图像中水域呈现低灰度、灰度起伏小特性。该文从实时性出发,采用1维Otsu算法分割SAR图像。证明了两种典型Otsu分割算法的阈值关系,提出了分割性能评估方法,并通过实测SAR图像对比分析了多阈值分割与单阈值递归分割的水域分割性能。结果表明,与单阈值递归分割相比,多阈值分割能够更好地提取水域轮廓,且漏警率低,可应用于桥梁自动目标识别和景象匹配等方面。

海上溢油SAR图像分割算法研究

运用SAR进行海上溢油监测是目前主要的手段。本文采用ENVISAR-ASAR数据,在分析溢油特性基础上,基于专家知识,开展一种区域种子扩散的方法进行图像分割研究,为溢油信息自动提取打下基础。该方法能够有效的分割出溢油信息,采用不同的预值,产生不同的结果,这与专家知识有关,这在一定程度上使分割结果受到人为影响,并且在分割后的图像的平滑与形状上存在误差,这将在以后的研究中进一步研究。