CBERS-1 PSF估计与图像复原

分析了遥感图像获取过程中导致图像退化模糊的原因。讨论了一种借助图像中的特定线状目标信息(如桥梁、堤坝等)，采用经验拟合的方式提取图像获取、传输过程中的点扩散函数，并利用该点扩散函数结合频域维纳滤波器求解去图像模糊的空域反卷积算子。将该方法应用于中巴资源卫星一号(CBERS-1)图像，图像质量得到明显的提高，收到了较好的复原效果。     

Singular Unit Restoration in Interferograms Based on Complex-Valued Markov Random Field Model for Phase Unwrapping

In generating a digital elevation map from an interferogram obtained by interferometric synthetic aperture radar, the filtering process is as important as the phase unwrapping process. Before unwrapping, we usually have to restore the data image by reducing so-called singular points (SPs) by filtering process without destroying or smearing delicate fringes. Previously, an effective SP restoration method was proposed based on the complex-valued Markov random field (CMRF) model. However, there is still room for improvement in the definition of SPs and in the formulation of CMRF parameter estimation. In this letter, we propose a novel scheme by introducing a new concept, namely, the ldquosingular unit.rdquo We also estimate CMRF parameters locally as a weighted sum by taking the distance and SP numbers in sample sites into account. By using this restoration method, we demonstrate a high-performance removal of SPs. We also find that delicate landscape features, which are often lost in conventional filtering, are preserved appropriately. We confirm the quality in higher signal-to-noise ratios obtained against actual height data.     

基于SAR-SIFT改进的SAR图像配准

合成孔径雷达(SAR)图像配准时,由于受到乘性散斑影响,匹配算法的性能受到限制.SAR-SIFT使用ROEWA算子代替差分算法计算梯度,对SAR图像的散斑具有一定的稳健性.为了进一步抑制散斑噪声,本文提出了基于SAR-SIFT改进的SAR图像配准算法.利用非线性扩散滤波生成SAR图像的非线性扩散尺度空间,在非线性尺度空...     

Overlapped rectangle image representation and its application to exact legendre moments computation

Linear quadtree is a popular image representation method due to its convenient imaging procedure. However, the excessive emphasis on the symmetry of segmentation, i.e. dividing repeatedly a square into four equal sub-squares, makes linear quadtree not an optimal representation. In this paper, a no-loss image representation, referred to as Overlapped Rectangle Image Representation (ORIR), is presented to support fast image operations such as Legendre moments computation. The ORIR doesn’t importune the symmetry of segmentation, and it is capable of representing, by using an identical rectangle, the information of the pixels which are not even adjacent to each other in the sense of 4-neighbor and 8-neighbor. Hence, compared with the linear quadtree, the ORIR significantly reduces the number of rectangles required to represent an image. Based on the ORIR, an algorithm for exact Legendre moments computation is presented. The theoretical analysis and the experimental results show that the ORIR-based algorithm for exact Legendre moments computation is faster than the conventional exact algorithms.     

重叠矩形图像表示及其上的精确Legendre矩计算(英文)

Linear quadtree is a popular image representation method due to its convenient imaging procedure. However, the excessive emphasis on the symmetry of segmentation, i.e. dividing repeatedly a square into four equal sub-squares, makes linear quadtree not an optimal representation. In this paper, a no-loss image representation, referred to as Overlapped Rectangle Image Representation （ORIR）, is presented to support fast image operations such as Legendre moments computation. The ORIR doesn＇t importune the symmetry of segmentation, and it is capable of representing, by using an identical rectangle, the information of the pixels which are not even adjacent to each other in the sense of 4-neighbor and 8-neighbor. Hence, compared with the linear quadtree, the ORIR significantly reduces the number of rectangles required to represent an image. Based on the ORIR, an algorithm for exact Legendre moments computation is presented. The theoretical analysis and the experimental results show that the ORIR-based algorithm for exact Legendre moments computation is faster than the conventional exact algorithms.     

结合BM3D和多级非线性加权平均中值滤波的遥感影像混合噪声去噪方法

提出了一种基于三维块匹配（BM3D）和多级非线性加权平均中值滤波的遥感影像混合噪声去噪方法,使用coief3小波和sym2小波替换传统三维块匹配算法中的bior1.5和hear参数,将三维块匹配算法和多级非线性加权平均中值滤波相结合以提高算法对混合噪声的去噪能力。进行一个仿真对比试验和一个真实遥感影像去噪试验,试验表明,本文提出的方法在遥感影像混合噪声去噪方面效果要优于传统混合噪声去噪方法。     

联合EEMD-KECA算法的InSAR干涉相位时频滤波

根据干涉图信号和噪声时频分布差异的特点,提出一种改进的基于经验模态分解EEMD的InSAR干涉相位滤波方法。该方法首先利用可有效降低模态混叠的EEMD算法,对干涉图的实部及虚部分别进行2维经验模态分解,获得具有不同时间尺度的模态分量;然后根据信号和噪声分量的时间尺度分布特性的差异,采用适用于非线性信号分析的KECA算法对噪声识别、分离;最后利用去除噪声后的模态分量重构干涉图。为了证明本文方法的有效性,分别利用模拟数据及真实InSAR差分干涉相位进行滤波试验。对比本文EEMD-KECA滤波方法、Goldstein滤波、圆周期—中值滤波、EMD分解、EMD-PCA方法的滤波效果,采用相干斑指数、均方差指数、边缘保持指数进行定量评价。结果表明,与经典InSAR干涉图滤波方法相比,本文联合EEMD-KECA算法的滤波方法能有效滤除干涉图噪声,且在条纹边缘等细节信息的保持上也具有较大优势。     

多光谱遥感图像的自适应各向异性扩散滤波

图像滤波预处理不仅有助于增强图像信噪比和减少类间(intra-c lass)光谱可变性,而且还能够对影像中亮度均匀的区域进行平滑,从而为影像地物识别、分割和分类提供有力的支持。在Pope和Acton提出的两个多光谱图像各向异性扩散滤波模型的基础上,通过各向异性扩散和稳健统计学的联系,建立了基于B iwe ight Estim ator误差模型的扩散系数,同时利用非线性退化技术对梯度阈值的改进,提出了两个基于各向异性扩散方程的非线性滤波方法。提出的方法不仅能够有效地消除传感器随机噪声的影响,而且还能够很好地保持遥感图像上重要的细节边缘和影像质量。实验结果表明,不论是视觉效果还是质量统计分析,提出的扩散模型的性能优于Pope和Acton的各向异性扩散模型,是理想的多光谱图像保边缘滤波方法。     

InSAR干涉图最优化方向融合滤波

在分析干涉条纹图噪声特征的基础上, 提出了一种能有效保持干涉图条纹的边缘和细节信息的InSAR干涉图滤波算法——基于最优化融合的自适应方向平滑算法。该算法首先对8个线性方向窗口进行统计分析, 然后根据干涉图的相干性选择合适的线性方向窗口, 按照最优化融合的方法, 以方差倒数为权重对各个方向窗口的均值进行加权平均。模拟和真实干涉图数据的实验结果表明新方法不仅能有效的抑制干涉图噪声, 而且能较好的保持干涉图的细节和边缘信息, 具有很好的信息保持能力。     

Ramoving Non—uniform Illumination Effects from Deep—sea Floor Images

The deep-sea floor images are acquired under non-uniform illumination,the effects of which bring up many difficulties for image segmentation.The paper compares three methods of removing non-uniform illumination effects.The effectiveness and robustness are evaluated on three test images with high-,moderate- and low-density grains,respectively.The results show that local filtering algorithm does not remove the non-uniform illumination effects completely.The image subtracting algorithm will lose some of the dynamic range of the original data.The enhanced image stretching algorithm is the most effective one.     

基于背景匀质性双边滤波的SAR图像斑点噪声抑制算法

针对双边滤波在抑制SAR图像相干斑噪声的不足,本文提出了一种基于背景匀质性的改进双边滤波算法BH-IBF(Improved Bilateral Filtering algorithm based on Background Homogeneity),并将其应用于SAR 图像斑点噪声抑制.BH-IBF以传统双边滤波作为基...     

顾及线性化模型误差补偿的卡尔曼滤波算法

针对扩展卡尔曼滤波（EKF）线性化所产生的线性化模型误差问题,使用非线性预测滤波对线性化所引起的模型误差进行预测,并在标准EKF的解算过程中考虑到预测所得误差的统计特性,使模型更趋于真实情况。通过算例对改进算法的性能进行了验证。     

Nonorthogonal Tensor Matricization for Hyperspectral Image Filtering

A generalized multidimensional Wiener filter for denoising is adapted to hyperspectral images (HSIs). multidimensional wiener filtering (MWF) uses the signal subspace of each n-mode flattening matrix of the HSI, which is a third-order tensor. However, in the HSI case, the n-mode ranks are close to the n-mode dimensions. Thus, the signal subspace dimension can be underestimated. This leads to a loss of spatial resolution-edge blurring-and artifacts in the restored HSI. To cope with the underestimation while preserving edges, a new method is proposed. It estimates the relevant directions of flattening that may not be parallel to HSI dimensions. We adapt the bidimensional straight line detection algorithm that estimates the HSI main directions, which are used to flatten the HSI tensor. We also generalize the quadtree decomposition to tensors in order to adapt the filtering to the local image characteristics. Comparative studies with MWF, principal component analysis-stationary wavelet transform, and channel-by-channel Wiener filtering show that our algorithm provides better performance while restoring impaired HYDICE HSIs.     

Efficient Directional Gaussian Smoothers

Linear and nonlinear filters, including morphological operators, play a significant role in the processing of remote sensing imagery. In particular, smoothing filters have been extensively used for noise removal and image restoration. In applications where linear and shift-invariant filters can be effectively employed, filtering is computationally efficient if implemented in transform domains. Nevertheless, in remote sensing applications, it is essential that smoothing filters be capable of handling missing and erroneous data without loss of information. In such cases, filtering requires the involvement of logical operations in order to determine which pixels should be used for processing, and thus takes the form of a nonlinear operator. Hence, transform-based methods cannot be used. Still, in applications where large volumes of data need to be processed, it is greatly desired that fast filtering algorithms are used. This letter introduces a computationally efficient spatial-domain-based implementation which is partially separable and steerable. The technique is general, and its efficiency has been demonstrated on weather radar data. It is shown that the proposed filtering approach is significantly faster compared to a recently introduced separable filter implementation.      

Object-based image analysis through nonlinear scale-space filtering

In this research, an object-oriented image classification framework was developed which incorporates nonlinear scale-space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. Image objects were computed at various scales and were connected to a kernel-based learning machine for the classification of various earth-observation data from both active and passive remote sensing sensors. Unlike previous object-based image analysis approaches, the scale hierarchy is implicitly derived from scale-space representation properties. The developed approach does not require the tuning of any parameter—of those which control the multi-scale segmentation and object extraction procedure, like shape, color, texture, etc. The developed object-oriented image classification framework was applied on a number of remote sensing data from different airborne and spaceborne sensors including SAR images, high and very high resolution panchromatic and multispectral aerial and satellite datasets. The very promising experimental results along with the performed qualitative and quantitative evaluation demonstrate the potential of the proposed approach.     

大失准角的SINS/GPS组合对准算法研究

利用四元数误差方程和非线性滤波技术能较好地解决大失准角下SINS的空中对准问题。迭代滤波比扩展卡尔曼滤波能在更大程度上改善对准精度，但计算量大。针对此不足，本文基于扩展卡尔曼滤波的状态与偏差解耦算法具有较高数值效率和迭代滤波具有较高精度的特点，推导出了一种非线性滤波算法，并对基于加性四元数误差方程的SINS／GPS组合对准进行了数字仿真。仿真结果表明：该算法既具有迭代滤波的精度又比迭代滤波计算量小。     

联合稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类

零样本影像分类技术旨在通过学习数据集的部分类别（可见类）,获得识别在训练阶段未出现类别（不可见类）的能力。该技术在遥感大数据时代具有重要现实意义。目前,遥感领域的零样本场景分类方法对于映射后的语义空间优化关注很少,导致已有方法的整体分类性能较差。基于这一考虑,本文提出了一种基于稳健跨域映射和渐进语义基准修正的零样本遥感影像场景分类方法。在训练的有监督学习模块,基于可见类的类别语义向量和场景影像样本,实现深度特征提取器学习和视觉空间到语义空间的稳健映射。在训练的无监督学习阶段,基于全体类别的类别语义向量和不可见类遥感影像样本,分别通过协同表示学习和k近邻算法来渐进修正不可见类类别的语义向量,从而缓解可见类语义空间与不可见类语义空间的漂移问题和自编码跨域映射模型映射后不可见类语义空间与协同表示后不可见类语义空间的偏移问题。在测试阶段,基于学习所得的深度特征提取器、自编码跨域映射模型和修正后的不见类语义向量,实现对不可见类遥感影像场景的分类。本文整合多个已有公开的遥感影像场景数据集,组建了一个新的遥感影像场景数据集,在此数据集上进行试验。试验结果表明本文提出的算法在多种不同的可见类与不可见类的划分情况下都明显优于已有公开零样本分类方法。     

采用张量子空间的高光谱影像多维滤波算法

本文提出了一种基于张量子空间的多维滤波算法,将其应用于高光谱遥感影像降噪。该方法将高光谱影像数据视为三阶张量,引入张量数据表达,通过张量子空间分解将含噪影像投影到信号子空间,根据影像信号与噪声在子空间中分布的不同滤除噪声并保留原始影像的信号成分。利用该算法作用于多组含噪高光谱数据,对比逐波段二维维纳滤波算法、小波降噪算法等传统数字图像降噪算法的结果,实验证明了这种新型降噪算法的有效性。     

一种改进的SAR干涉图综合滤波算法研究

SAR干涉图作为相位信息的载体,其质量直接影响对研究区域形变状况的进一步分析,采取有效的滤波算法能抑制干涉图相位噪声,提高干涉测量精度。在获得的干涉相位图中,由于矿区开采而造成的地表沉降会体现出近环状相位条纹的特征。针对这一特点,对传统的基于梯度的滤波算法做出了改进,并结合Goldstein频域滤波和改进的梯度自适应滤波,提出了一种适用于矿区沉降形成的SAR干涉相位模式滤波方法。选取河北峰峰煤矿的PALSAR干涉相位图作为实验数据,对该滤波方法做出了详细的性能评价和对比。结果表明,采用本文提出的综合滤波方法在显著降低实验区SAR干涉图相位噪声的同时,也很好地保持了相位分辨率,使由于矿区沉降而造成的形变相位环的边缘形态更加清晰。     

一种使用剪切波变换的干涉图滤波算法

干涉图小波阈值法滤波未考虑干涉相位统计特性, 导致在低相干区域得到的滤波效果不能令人满意。针对这一问题, 提出了一种剪切波变换与干涉相位标准差相结合的相位滤波算法。该算法将干涉相位统计特性与剪切波阈值滤波结合起来, 利用相位标准差改正滤波阈值以提高滤波效果。此外, 为了评价干涉图的滤波效果并为实测数据选择合适的滤波方法提供参考, 将模拟干涉图解缠结果的局部均方差分布作为滤波质量评价指标。将此算法与Goldstein滤波、小波滤波、最优方向融合滤波和剪切波软阈值滤波进行比较, 结果表明所提算法能更有效地削弱干涉图噪声, 同时保留干涉图的细节信息, 避免了低相干地区弱滤波问题。