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.

PAN-DeSpeck:A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

SAR images commonly suffer fromspeckle noise,posing a significant challenge in their analysis and interpretation.Existing convolutional neural network(CNN)based despeckling methods have shown great performance in removing speckle noise.However,these CNN-basedmethods have a fewlimitations.They do not decouple complex background information in amulti-resolutionmanner.Moreover,they have deep network structures thatmay result in many parameters,limiting their applicability tomobile devices.Furthermore,extracting key speckle information in the presence of complex background is also a major problem with SAR.The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling(PAN-Despeck)network.The primary objective is to enhance image quality and enable improved information interpretation,particularly on mobile devices and scenarios involving complex backgrounds.The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis.By utilizing this approach,complex background information can be effectively decoupled,leading to enhanced despeckling performance.Furthermore,the attention mechanism selectively focuses on key speckle features and facilitates complex background removal.The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed,making it lightweight while maintaining high performance.Through comprehensive evaluations,it is demonstrated that PAN-Despeck outperforms existing image restoration methods.With an impressive average peak signal-to-noise ratio(PSNR)of 28.355114 and a remarkable structural similarity index(SSIM)of 0.905467,it demonstrates exceptional performance in effectively reducing speckle noise in SAR images.The source code for the PAN-DeSpeck network is available on GitHub.

SAR image despeckling via Lp norm regularization

Synthetic aperture radar(SAR) image despeckling has been an attractive problem in remote sensing.The main challenge is to suppress speckle while preserving edges and preventing unnatural artifacts(such as annoying artifacts in homogeneous regions and over-smoothed edges).To address these problems,this paper proposes a new variational model with a nonconvex nonsmooth Lp(0 <p<1) norm regularization.It incorporates Lp(0<p<1) norm regularization and I-divergence fidelity term.Due to the nonconvex nonsmooth property,the regularization can better recover neat edges and homogeneous regions.The Ⅰ-divergence fidelity term is used to suppress the multiplicative noise effectively.Moreover,based on variable-splitting and alternating direction method of multipliers(ADMM) method,an efficient algorithm is proposed for solving this model.Intensive experimental results demonstrate that nonconvex nonsmooth model is superior to other state-of-the-art approaches qualitatively and quantitatively.

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.

SAR image de-noising via grouping-based PCA and guided filter

A novel synthetic aperture radar(SAR)image de-noising method based on the local pixel grouping(LPG)principal component analysis(PCA)and guided filter is proposed.This method contains two steps.In the first step,we process the noisy image by coarse filters,which can suppress the speckle effectively.The original SAR image is transformed into the additive noise model by logarithmic transform with deviation correction.Then,we use the pixel and its nearest neighbors as a vector to select training samples from the local window by LPG based on the block similar matching.The LPG method ensures that only the similar sample patches are used in the local statistical calculation of PCA transform estimation,so that the local features of the image can be well preserved after coefficients shrinkage in the PCA domain.In the second step,we do the guided filtering which can effectively eliminate small artifacts left over from the coarse filtering.Experimental results of simulated and real SAR images show that the proposed method outstrips the state-of-the-art image de-noising methods in the peak signalto-noise ratio(PSNR),the structural similarity(SSIM)index and the equivalent number of looks(ENLs),and is of perceived image quality.

ICA Based Speckle Filtering for Target Extraction in SAR Images Using Adaptive Space Separation

A novel approach based on independent component analysis (ICA) for speckle filtering and target extraction of synthetic aperture radar (SAR) images is proposed using adaptive space separation with weighted information entropy (WIE) incorporated. First the basis and the independent components are respectively obtained by ICA technique, and WIE of the image is computed; then based on the threshold computed from function T-WIE (threshold versus weighted-information-entropy), independent components are adaptively separated and the bases are classified accordingly. Thus, the image space is separated into two subspaces: "clean" and "noise". Then, a proposed nonlinear operator ABO is applied on each component of the 'clean' subspace for further optimization. Finally, recovery image is obtained reconstructing this subspace and target is easily extracted with binarisation. Note that here T-WIE is an interpolated function based on several representative target SAR images using proposed space separation algorithm.

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。

一种非下采样轮廓波变换的机载SAR图像增强

针对机载合成孔径雷达(SAR)影像因入射角、侧视距离和后向散射系数差异导致的内部亮度不均衡、边缘细节特征模糊等图像质量问题,采用一种基于非下采样轮廓波变换(NSCT)的图像增强处理方法来提升视觉效果,增强图像质量。相对于传统图像增强算法,该方法能够更好地解决SAR影像的辐射差异:结构相似度与平均梯度表现最优,在较好保持原始影像信息的基础上,亮度不均匀现象得到有效改善,对机载SAR影像的视觉效果有很大的提高。

一种改进的基于惯导运动补偿的实时成像方法

合成孔径雷达(SAR)作为一种主动式的对地微波遥感技术,具有全天候、全天时和远距离的特点。通过发射大带宽信号,获得距离向的高分辨。通过平台相对静止场景的运动形成合成孔径,获得方位向的高分辨,SAR平台的运动是成像的基本条件。对于机载SAR,由于载机在大气中飞行时受到大气湍流等因素的影响,不可能保持理想的运动状态,必然会产生运动误差,运动误差会导致相位误差和包络移动,从而导致图像散焦等问题。如何对SAR进行运动补偿是实现成像的关键。基于惯导数据的运动补偿是实现高分辨成像的重要手段之一。根据惯导数据的特点,结合卡尔曼滤波原理和SAR成像的几何模型,提出了一种基于卡尔曼滤波的惯导数据滤波和重采样方法。该方法可以有效剔除运动参数野值,精确地获得雷达平台的运动参数。文中所提出的运动补偿方法能和SAR成像算法很好的结合,实测数据的成像结果验证了该方法的准确性和有效性。

基于各向异性扩散的SAR图像斑点噪声滤波算法

在SAR(Synthetic Aperture Radar)图像噪声抑制处理中,为了有效地保持图像边缘,作者在斑点噪声去除的各向异性扩散模型(SRAD模型)的基础上,提出了一个基于各向异性扩散的SAR图像斑点噪声滤波算法.该算法对应的扩散系数从理论上满足Charbonnier等人提出的构造扩散系数准则,同时该算法能够通过对边缘直方图上累计百分比和相对信噪比阈值进行调节来得到一系列不同的滤波效果,从而满足不同的应用需求,如绘图、高分辨率或细节丰富的处理结果.实验结果表明,与传统的方法相比,该算法不论从噪声去除能力、边缘和纹理保持能力上,还是从视觉评价效果来看,都具有一定的优越性.

考虑SAR图像导航量测特性的SAR/INS组合导航非线性滤波

SAR/INS组合导航系统中,当SAR量测输出时间间隔较长时,惯性导航得不到及时修正,系统将具有较强的非线性特性,导致常规卡尔曼滤波算法的精度受到影响甚至滤波发散.为了提高组合导航的精度,该文结合惯性导航系统的非线性模型,设计基于无迹卡尔曼滤波(UKF)解决非等间隔量测修正的SAR/INS组合导航非线性滤波算法,并通过协方差分析对比研究常规卡尔曼滤波和该文算法的性能.仿真结果验证了该文算法具有更高的精度.

SAR图像斑点噪声抑制与滤除技术

论述了不同时期各种合成孔径雷达（ＳＡＲ）图像斑点噪声（ｓｐｅｃｋｌｅ）抑制与滤除技术，并且对各种斑点噪声（ｓｐｅｃｋｌｅ）抑制与滤除算法的特点进行分析。比较了不同算法的优缺点，提出利用多尺度分析小波分析发展滤除斑点噪声新算法，提高ＳＡＲ的灰度分辨率和空间分辨率，是很有意义的方向之一。

SAR图像相干斑抑制研究进展

相干斑抑制是SAR图像处理领域的研究热点之一,也是SAR图像解译和应用中的关键步骤,因此SAR图像的相干斑抑制算法具有重要的研究价值。在简要介绍SAR图像相干斑的产生机理和数学模型的基础上,综述了国内外相干斑抑制的最新研究成果,重点分析了空域滤波和变换域滤波两类方法。从算法的可行性角度出发,分析了几种具有代表性的相干斑抑制方法及其优缺点,总结了常用相干斑抑制效果评价指标,最后对今后工作方向进行了展望。

星载SAR图像的斑点噪声抑制与滤波研究

根据斑点噪声的来源,分析和比较了抑制SAR图像斑点噪声的Lee滤波、Gamma MAP滤波、Frost滤波算法,并用台湾地区的SAR图像数据验证了不同滤波算法对斑点噪声的抑制效果.结果表明,Lee滤波抑制斑点噪声的综合性能最好.

抑制SAR图像相干斑的迭代方向滤波算法

为保护SAR图像边缘特征并有效提高对乘性相干斑噪声的抑制性能,该文提出一种基于迭代方向滤波的抑制图像相干斑新算法。该算法先借助高斯-伽马平行窗估计出的比率边缘强度映射(ESM)与方向信息,自适应地控制各向异性高斯核(AGK),生成沿ESM方向分布的具有各向异性支撑区域的局域窗。然后将SAR图像多种局部统计参量联合作为衰减因子,形成与SAR图像区域分布特性相适应的负指数衰减型加权系数,进而将负指数衰减型加权系数与局域窗带方向的各向异性支撑区域结合形成局域加权的方向滤波。最后对SAR图像迭代地进行方向滤波即可实现带边缘保护的相干斑抑制。实验结果表明,与多种抑斑算法相比,该文算法在SAR图像抑斑与边缘保护方面均获得了更好的性能。

基于Chan-Vese模型的SAR图像分割

由于SAR图像存在较强的斑点噪声,使用Chan-Vese模型水平集分割方法会产生很多误分割。同时,水平集解法存在计算量大、分割速度慢的问题。在Chan-Vese模型基础上,增加新的内能项——距离正则项,得到了一种改进的曲线演化模型。避免了水平集函数的周期性更新,具有更大的迭代步长,从而加快分割速度,并且提高Chan-Vese模型的抗噪性。对该模型采用人工合成图像和真实SAR图像进行分割实验,通过比较,可看出改进模型具有较高的数值精度和较快的分割速度。对于噪声很强的图像,使用增强Lee滤波进行预处理,可以进一步提高改进模型的分割速度和效果。实验结果表明:改进Chan-Vese模型能高效快速地完成SAR图像分割,具有较高的抗噪性。

星载SAR图像斑点抑制与质量评估系统

结合星载合成孔径雷达 (SAR)单视图像斑点噪声抑制技术与质量评估 ,并基于为科学数据可视化及数学分析提供完备计算环境的IDL(InteractiveDataLanguage)语言 ,完成了在IDL环境下开发的SAR图像斑点抑制与质量评估系统 ,获得了高空间分辨率和高辐射分辨率的星载SAR图像 .

一种抑制SAR图像相干斑的空域滤波算法

为在保护SAR图像边缘特征的同时有效抑制乘性相干斑噪声,提出了一种空域滤波新算法。该算法以负指数衰减型加权滤波模型为基础,通过将SAR图像多种局部统计参量巧妙结合作为联合衰减因子,形成与SAR图像区域分布特性相适应的负指数型加权系数;同时采取两次滤波策略,先由预滤波削弱SAR图像相干斑噪声并估计获得更精准的局部统计参量,然后借助精细局部统计参量再对原SAR图像实施精细滤波。实验结果表明,与多种抑斑算法相比,该算法在SAR图像抑斑与边缘保护方面均获得了更好的性能。

SAR图像弱反射地物边缘提取方法

提出了一种抑制ＳＡＲ图像相干斑噪声和提取弱反射地物边缘方法．传统噪声抑制和小波变换去噪方法有其不足之处，本文将小波变换和维纳滤波结合抑制ＳＡＲ图像相干斑噪声，通过选择恰当小波基获得良好滤波效果。采用最小错误准则计算ＳＡＲ图像的理论分割阈值，通过逐次迭代得到其合理大小，利用形态算子作用于分割图像获得其边缘．实际ＳＡＲ图像测试结果表明了本文方法的有效性．

基于局部混合滤波的SAR图像去噪

相干斑噪声是合成孔径雷达(synthetic aperture radar,SAR)成像系统所固有的缺点,严重影响SAR图像的可用性,给后续的图像分割、特征提取和目标识别等工作带来严峻的挑战。结合非下采样方向滤波器和双树复小波变换各自的特点,提出一种新的基于非下采样方向滤波-双树复小波变换的局部混合滤波SAR图像去噪算法,具有多方向和多尺度性,保持了图像的平移不变性,改善了图像的视觉效果。与其他算法不同,本文算法采用非下采样方向滤波器级联双树复小波的方法,不仅对每次产生的高频分量进行去噪,还对变换所产生的低频分量进行滤波去噪。实验结果表明:与使用同级双树复小波-轮廓波变换加软阈值去噪相比,本文算法的峰值信噪比提高2dB;与使用轮廓波加循环平移(cycle spinning,CS)软阈值算法去噪相比,本文算法去噪后的图像不仅峰值信噪比有所提高,而且去噪后的图像更为平滑,抑制了人造纹理产生,视觉效果得到了明显改善。