Speckle suppression in synthetic aperture radar ocean internal solitary wave images with curvelet transform

This paper proposes a speckle-suppression method for ocean internal solitary wave（ISW） synthetic aperture radar（SAR） images by using the curvelet transform.The band-shaped signatures of ocean ISWs in SAR images show obvious scale and directional characteristics.The curvelet transform possesses a very high scale and directional sensitivity.Therefore,the curvelet transform is very efficient in analyzing wave signals in SAR images.A noisy ocean ISW SAR image can be decomposed at different scales,directions,and positions using the curvelet transform.The information of the ISWs is centralized in the curvelet coefficients of certain directions under certain scales,whereas the speckle noise is distributed in every scale and direction.By manipulating the curvelet coefficients,the signals of the ISWs can be extracted from the noisy SAR image.Finally,the speckle noise is suppressed and the ISW feature is enhanced by adding the signals of the ISWs back to the original SAR image.Experiments demonstrate the effectiveness of this method.

An accelerated nonlocal means algorithm for synthetic aperture radar ocean image despeckling

Synthetic aperture radar(SAR)images play an increasingly important role in ocean environmental monitoring and research.However,SAR images are inherently corrupted by speckle noise.SAR ocean images have some unique characteristics.The signatures of ocean phenomena in SAR images mainly exhibit as stripe or plaque shaped features.These features typically have a high degree of self-similarity or redundancy.The nonlocal means(NLM)method can measure the structural similarity between different image patches and take advantage of redundant information in images.Considering that the NLM algorithm is computationally intensive and timeconsuming,an accelerated NLM algorithm for SAR ocean image despeckling is proposed in this paper.A method is used to discriminate between texture and flat pixels in SAR images.Large similarity and search windows are used on texture pixels,whereas small similarity and search windows are used on flat pixels.Furthermore,the improved NLM algorithm is accelerated by a graphic processing unit(GPU)based on the compute unified device architecture(CUDA)parallel computation framework.The computational efficiency is improved by approximately 200 times.

WATERMARKING WITH REFINED PERCEPTUAL MASKING TUNED FOR SYNTHETIC APERTURE RADAR IMAGES

A watermarking scheme designed for remote sensing images needs to meet the same demand of both invisibility as for ordinary digital images. Due to specific perceptual characteristics of Synthetic Aperture Radar(SAR) images, the watermarking algorithms with consideration of Human Vision System(HVS) modeling from optical images give poor performance when applied on SAR images. This paper examines a variety of factors affecting the noise sensitivity, and further proposes a refined pixel-wise masking approach for watermarking on SAR images. The proposed approach is applied on logarithmic transformed SAR images, and has increased the acceptable watermark embedding strength by about 6 dB to 10 dB while achieving the same levels of watermarked image visual quality. Experimental results show that this approach enhanced the perceptual invisibility of watermarking based on wavelet decomposition.

基于粗糙集理论的SAR图像speckle滤波方法

针对合成孔径雷达(SAR)采用主动式相干波的成像方式,在从原始信号到图像的重建过程中存在斑点噪声的干扰的问题,根据粗糙集理论的条件属性把SAR图像像素分成三类:均匀区域类、非均匀区域类和包含分离点目标类.对不同的像素类采用不同的滤波方式,然后合并三类子图像得到SAR图像的speckle滤波的图像.该方法既可除去图像中的斑点噪声,又可保留图像中的细节特征,有利于对图像的后续处理.

Suppression of Speckle in SAR Images Using Wavelet-Based HMM

In order to suppress the speckle appearing in synthesis aperture radar (SAR) images, a novel speckle reduction method based on wavelet domain hidden Markov tree (HMT) was proposed. First, the image was logarithmic transformed to change the statistical property of the speckles. Then an HMT was constructed in the correspondent wavelet domain. Based on this model, the image signal was restored by maximum likelihood estimation and speckle noise was suppressed. Simulating SAR images had shown that the performance of the filter is satisfactory for both speckle smoothing and edges presentation, and for generating visually natural images as well.

Novel polarimetric SAR speckle filtering algorithm based on mean shift

For better interpretation of synthetic aperture radar（SAR） images,the speckle filtering is an important issue.In the area of speckle filtering,the proper averaging of samples with similar scattering characteristics is of great importance.However,existing filtering algorithms are either lack of a similarity judgment of scattering characteristics or using only intensity information for similarity judgment.A novel polarimetric SAR（PolSAR） speckle filtering algorithm based on the mean shift theory is proposed.As polarimetric covariance matrices or coherency matrices form Riemannian manifold,the pixels with similar scattering characteristics gather closely and those with different scattering characteristics separate in this hyperspace.By using the range-spatial joint mean shift theory in Riemannian manifold,the pixels chosen for averaging are ensured to be close not only in scattering characteristics but also in the spatial domain.German Aerospace Center（DLR） L-Band Experiment SAR（E-SAR） data and East China Research Institute of Electronic Engineering（ECRIEE） PolSAR data are used to demonstrate the efficiency of the proposed algorithm.The filtering results of two commonly used speckle filtering algorithms,refined Lee filtering algorithm and intensity driven adaptive neighborhood（IDAN） filtering algorithm,are also presented for the comparison purpose.Experiment results show that the proposed speckle filtering algorithm achieves a good performance in terms of speckle filtering,edge protection as well as polarimetric characteristics preservation.

Improved SMB speckle filtering of polarimetric SAR data with synergistic use of orientation angle compensation and spatial majority rule

The scattering-model-based(SMB)speckle filtering for polarimetric SAR(Pol SAR)data is reasonably effective in preserving dominant scattering mechanisms.However,the efficiency strongly depends on the accuracies of both the decomposition and classification of the scattering properties.In addition,a relatively weak speckle reduction particularly in distributed media was reported in the related literatures.In this work,an improved SMB filtering strategy is proposed considering the aforementioned deficiencies.First,the orientation angle compensation is incorporated into the SMB filtering process to remedy the overestimation of the volume scattering contribution in the Freeman-Durden decomposition.In addition,an algorithm to select the homogenous pixels is developed based on the spatial majority rule for adaptive speckle reduction.We demonstrate the superiority of the proposed methods in terms of scattering property preservation and speckle noise reduction using L-band Pol SAR data sets of San Francisco that were acquired by the NASA/JPL airborne SAR(AIRSAR)system.

SUPPRESSING THE COHERENT SPECKLE OF SAR IMAGES WITH WAVELET ANALYSIS

This paper studies Synthetic Aperture Radar (SAR) images contaminated by the coherent speckle noise with the multiresolution analysis of wavelet transform. This study shows that the influences of the speckle on different frequency components of the SAR image are different, and that the SAR image and the speckle have different manners of singularity. So, this paper presents a denoising method of wavelet analysis to reduce the speckle. Some experiments approve that this method not only suppresses the speckle effectively, but also preserves as much target characteristics of the original image as possible. It shows that this denoising method of wavelet analysis offers a very attractive alternative to suppress the coherent speckle noise of the SAR image.

Speckle filtering of Synthetic Aperture Radar images using filters with object-size-adapted windows

Speckle degrades the radiometric quality of a Synthetic Aperture Radar(SAR)image.Previous methods for speckle reduction have used a fixedsize window for filtering the entire image.This,however,may not be effective for the entire image,as land covers of different sizes require different filtering windows.In this paper,a novel method is proposed by which each pixel in the image is filtered with a window appropriate for the size of object within it.The real in-phase and the imaginary quadrature components of the SAR images determine the best window size and the pixels in the intensity image are filtered using their own optimal windows.The proposed method is presented for both singleand multi-polarized SAR images,and the results of several common filters that were modified are presented.This approach is applied to two RADARSAT-2 images:one over San Francisco,California,USA and the other over St.John’s,Newfoundland and Labrador,Canada,producing results that were similar to,or outperformed,comparable filters while retaining details and suppressing speckle effectively.While the method was successful for single-look intensity data,it offers great potential for multi-look and amplitude data as well.

基于动量迭代快速梯度符号的SAR-ATR深度神经网络黑盒攻击算法

合成孔径雷达自动目标识别(SAR-ATR)领域缺乏有效的黑盒攻击算法,为此,该文结合动量迭代快速梯度符号(MI-FGSM)思想提出了一种基于迁移的黑盒攻击算法。首先结合SAR图像特性进行随机斑点噪声变换,缓解模型对斑点噪声的过拟合,提高算法的泛化性能;然后设计了能够快速寻找最优梯度下降方向的ABN寻优器,通过模型梯度快速收敛提升算法攻击有效性;最后引入拟双曲动量算子获得稳定的模型梯度下降方向,使梯度在快速收敛过程中避免陷入局部最优,进一步增强对抗样本的黑盒攻击成功率。通过仿真实验表明,与现有的对抗攻击算法相比,该文算法在MSTAR和FUSAR-Ship数据集上对主流的SAR-ATR深度神经网络的集成模型黑盒攻击成功率分别提高了3%~55%和6.0%~57.5%,而且生成的对抗样本具有高度的隐蔽性。

基于零样本学习的单张SAR图像相干斑滤波方法

相干斑滤波是合成孔径雷达(Synthetic aperture radar,SAR)图像解译重要的预处理步骤。近年来,基于卷积神经网络(Convolutional neural network,CNN)的相干斑滤波方法得到了快速的发展。然而,基于监督学习的滤波方法缺乏无相干斑参考SAR图像作为真值,基于自监督学习的滤波方法大多需要同一场景的多时相SAR图像训练网络,但是这些额外的数据集在实际场景中较难获取。此外,自监督学习方法通常需要较大的训练数据集和较深的网络进行相干斑滤波,导致其计算复杂度较高。因此,本文提出了一种基于零样本学习的单张SAR图像相干斑滤波方法。该方法的核心思想是对待测试的单张SAR图像进行子视分解,选取与待测试SAR图像欧式距离最近的子视图像进行配对,理论上证明了使用配对的子视图像自监督训练网络能达到使用无相干斑参考SAR图像监督训练网络的滤波效果。因此,通过设计自监督损失函数快速训练轻量化相干斑滤波网络,将训练好的网络对待测试SAR图像进行滤波。相较于基于监督学习和自监督学习的相干斑滤波方法,本文所提方法不需要无相干斑参考或多时相SAR图像用于模型训练,也不需要额外训练数据,只需使用任意一个轻量化的CNN即可实现相干斑滤波。在Radarsat-2和ALOS-2实测数据上的实验结果表明,本文所提方法的参数量比对比方法低22倍,能更好的实现对匀质区域相干斑的抑制和图像细节的保护。

基于卷积神经网络的SAR图像舰船分类

针对合成孔径雷达图像中斑点噪声导致图像分类准确率低的问题,提出一种基于改进VGG16的分类算法。在卷积层中加入一层注意力层,专注于重要特征,抑制不重要特征,从而抑制斑点噪声。在目标函数中引入Fisher损失函数,用该函数对特征的类内距离和类间距离进行约束,从而使得由于斑点噪声所造成的分类错误减少。通过实验可知,相比于改进前的网络,分类准确率提高了5.63百分点,有效改善了因为斑点噪声所造成的分类准确率低的问题。

SAR图像相干斑抑制滤波性能评价

为了评价合成孔径雷达(syntheticapertureradar,SAR)图像相干斑滤波算法性能,提出了一个性能评价体系,其中包含9项指标:等效视数(或平滑指数)、辐射分辨率、信号杂波比损失、信号辐射度损失、均值偏差、空间分辨率损失、边缘保持指数、峰值旁瓣比偏差、积分旁瓣比偏差。该评价体系能同时衡量各滤波算法对相干斑的抑制程度以及对图像原始信息的保持程度。通过用真实图像和模拟SAR图像对典型相干斑滤波算法的检验,表明了评价指标的有效性。

一种改进的SAR图像边缘检测方法

针对Ratio边缘提取算法对弱边缘检测效果差 ,边缘定位精度低的缺点 ,提出了一种改进算法用于检测微弱边缘 ,并给出具有CFAR特性的阈值选取办法 实际应用中 ,将两种方法结合为一种综合处理方法 ,对真实SAR图像的处理结果表明 。

一种基于压缩感知的稀疏孔径SAR成像方法

高分辨大场景合成孔径雷达(SAR)成像给数据存储和传输系统带来沉重负担.本文针对条带式SAR成像,提出一种基于压缩感知技术的稀疏孔径SAR成像方法.该方法沿方位向以部分子孔径采样的方式获取降采样的原始数据,然后在距离向采用传统匹配滤波方法实现脉冲压缩处理,在方位向则利用小波基作为场景散射系数的稀疏基,并通过求解最小l1范数优化问题重构方位向散射系数.该方法在存在多普勒参数误差情况下,能够有效实现多普勒参数估计,具有良好稳健性.仿真和实测数据成像结果表明所提算法在方位向严重降采样条件下仍能够实现无模糊的SAR成像,具有较强的有效性与实用性.

小波包域值法去除合成孔径雷达图像斑点噪声

合成孔径雷达 (SAR)图像的斑点噪声使图像的解译能力明显降低 ,影响了SAR图像的应用 ,去除图像斑点噪声是SAR图像处理及应用的前提。去除的方法在图像域有很多 ,在频率域 ,也有基于FFT变换和小波变换的许多方法。文中根据沙漠地区的SAR图像的特点 ,对基于图像域和频率域的几种滤波方法进行了比较 ,提出了一种基于小波包变换的SAR图像斑点噪声去除方法 ,结果表明该方法既去除了斑点噪声 ,又保持了边缘特征信息。

合成孔径雷达图像统计滤波降噪方法

基于统计滤波方法需要在尽可能大的同质区域内进行滤波处理的要求 ,根据 MHR和 EPOS算法采用的可变窗口思想 ,提出了从八个不同方向上逐步扩大窗口 ,直到出现边界的搜索算法 ,在保持图像边界信息的同时滤除了斑点噪声的影响 .对于噪声统计量 ,提出了利用了比例图来估计其大小的方法 ,并由此求出搜索的域值 .实验结果表明 ,该方法处理的

小波域局部贝叶斯阈值的SAR图像斑点抑制算法

提出了一种抑制SAR图像斑点噪声的小波域贝叶斯软阈值方法。该算法不同于有偏的去除乘性噪声的同态滤波算法,而是将噪声转化为局部平稳的加性白噪声。在非下采样小波子带上,视数给定时该算法可以简洁有效地估计局部加性噪声方差。在实验中,该算法同Kuan,Lee和Argenti等算法作了比较,结果表明,在常用性能指标上所提算法优于其它算法。

利用区域划分的合成孔径雷达图像相干斑抑制算法

为在有效保护合成孔径雷达(SAR)图像边缘特征的同时进一步提高乘性相干斑噪声的抑制性能,提出了一种基于区域划分的SAR图像相干斑抑制(DABRS)算法.首先利用带方向的高斯-伽马平行窗通过比率运算生成边缘强度映射(ESM),并利用阈值化ESM对SAR图像进行区域划分,然后利用改进的变窗Kuan滤波与改进的Sigma滤波分别对SAR图像的均匀区域与边缘区域进行相干斑抑制.为进一步提高抑斑效果,两种改进抑斑算法均采用迭代滤波方式,且改进Kuan滤波的窗尺度随迭代次数的增加而增大.实验结果表明:与多种抑斑算法相比,DABRS算法在抑斑与边缘保护方面具有优势,等效视数提高超过30%,边缘保持指数提高8%以上,而且抑斑图像同质区还具有更平滑的视觉效果.