Interferometric SAR phase unwrapping using Green's formulation

Any method that permits retrieving full range (unwrapped) phase values starting from their (-π,π) determination (wrapped phase) can be defined as a phase unwrapping technique. This paper addresses a new procedure for phase unwrapping especially designed for interferometric synthetic aperture radar applications. The proposed algorithm is based on use of Green's first identity. Results on simulated as well as on real data are presented. They both confirm the excellent performance of the procedure     

Stochastic image denoising based on Markov-chain Monte Carlo sampling

A novel stochastic approach based on Markov-chain Monte Carlo sampling is investigated for the purpose of image denoising. The additive image denoising problem is formulated as a Bayesian least squares problem, where the goal is to estimate the denoised image given the noisy image as the measurement and an estimated posterior. The posterior is estimated using a nonparametric importance-weighted Markov-chain Monte Carlo sampling approach based on an adaptive Geman-McClure objective function. By learning the posterior in a nonparametric manner, the proposed Markov-chain Monte Carlo denoising (MCMCD) approach adapts in a flexible manner to the underlying image and noise statistics. Furthermore, the computational complexity of MCMCD is relatively low when compared to other published methods with similar denoising performance. The effectiveness of the MCMCD method at image denoising was investigated using additive Gaussian noise, and was found to achieve state-of-the-art denoising performance in terms of both peak signal-to-noise ratio (PSNR) and mean structural similarity (SSIM) metrics when compared to other published methods.     

Monte Carlo cluster refinement for noise robust image segmentation

This paper explores a stochastic approach to refining clustering results for data with spatial-feature context such as images under the presence of noise. We formulate the clustering problem as a maximum a posteriori (MAP) problem, and refine clustering results using importance-weighted Monte Carlo posterior estimates based on between-neighborhood error statistics to account for local spatial-feature context within a global framework. This cluster refinement approach is non-iterative and can be integrated with existing clustering methods to achieve improved clustering performance for image segmentation under high noise scenarios. Experiments on synthetic gray-level images, real-world natural images, and real-world satellite synthetic aperture radar imagery illustrate the proposed method’s potential for improving clustering performance of existing clustering algorithms for image segmentation under high noise situations.     

基于蒙特卡罗特征降维算法的小样本高光谱图像分类

高光谱图像分类是高光谱数据分析的重要研究内容.相关向量机由于不受梅西定理的限制、不需要设置惩罚因子等优势受到广泛关注.由于高光谱数据具有较高的维数,当训练样本较少时,高光谱数据的分类精度受到严重的影响.通常解决这种现象的办法是对原数据进行特征降维处理,然而多数基于filter模型的特征选择算法无法直接给出最优特征选择个数.为此提出利用蒙特卡罗随机实验可以对特征参量进行统计估计的特性,计算高光谱图像的最优降维特征数,并与相关向量机结合,对降维后的数据进行分类.实验结果表明了使用蒙特卡罗算法求解降维波段数的可靠性.相比较原始末降维数据,降维后的高光谱图像分类精度有较大幅度的提高.     

基于蒙特卡罗特征降维算法的小样本高光谱图像分类

高光谱图像分类是高光谱数据分析的重要研究内容。相关向量机由于不受梅西定理的限制、不需要设置惩罚因子等优势受到广泛关注。由于高光谱数据具有较高的维数, 当训练样本较少时,高光谱数据的分类精度受到严重的影响。通常解决这种现象的办法是对原数据进行特征降维处理,然而多数基于filter模型的特征选择算法无法直接给出最优特征选择个数。为此提出利用蒙特卡罗随机实验可以对特征参量进行统计估计的特性,计算高光谱图像的最优降维特征数,并与相关向量机结合,对降维后的数据进行分类。实验结果表明了使用蒙特卡罗算法求解降维波段数的可靠性。相比较原始未降维数据,降维后的高光谱图像分类精度有较大幅度的提高。     

Iterative image restoration using approximate inversepreconditioning

Removing a linear shift-invariant blur from a signal or image can be accomplished by inverse or Wiener filtering, or by an iterative least-squares deblurring procedure. Because of the ill-posed characteristics of the deconvolution problem, in the presence of noise, filtering methods often yield poor results. On the other hand, iterative methods often suffer from slow convergence at high spatial frequencies. This paper concerns solving deconvolution problems for atmospherically blurred images by the preconditioned conjugate gradient algorithm, where a new approximate inverse preconditioner is used to increase the rate of convergence. Theoretical results are established to show that fast convergence can be expected, and test results are reported for a ground-based astronomical imaging problem     

基于圆迹干涉SAR的DEM提取

圆迹干涉SAR具有对场景目标进行全方位观测测量的优势,为了满足高分辨率圆迹SAR成像对高精度的场景数字高程模型(Digital Elevation Model, DEM)的需求,该文提出了一种基于圆迹干涉SAR数据的高程估计方法。首先推导了采用后向投影(Back Projection, BP)算法时的圆迹干涉SAR信号模型,然后给出了基于圆迹干涉SAR处理的场景DEM提取流程,最后通过仿真实验验证了所提方法的有效性。     

SAR image despeckling using heavy-tailed Burr distribution

Multiplicative speckle noise diminishes the radiometric resolution of the synthetic aperture radar (SAR) images and all the coherent images. Speckle removal adds an extra value to an automated SAR image interpretation and analysis. In this paper, dual-tree complex wavelet-transform-based Bayesian method is proposed for despeckling the SAR images. In each subband, the reflectance and noise of the logarithmically transformed wavelet coefficients are modeled using heavy-tailed Burr and zero-mean Gaussian distributions. The closed-form expression for the shape parameter of Burr distribution is derived by employing the Mellin transform. The resultant complex-free quadratic maximum a posteriori solution with suitable shrinkage function yields despeckled SAR images. Extensive experiments are carried out using real SAR images as well as simulated images. The proposed method performs well in terms of equivalent number of looks with 3.5751 dB improvement in homogeneous region1 of Pipe river SAR image, edge preservation with 0.6158 improvement, peak signal to noise ratio of 51.3305 dB, and mean structural similarity index measure of 0.9397 at 0.05 noise variance for synthetically speckled image in comparison to the existing methods and takes averagely 2.3461 times less computing time. The proposed method provides a computationally efficient better speckle reduction in homogeneous regions while still preserving the edge.     

基于RPCA的SAR图像纹理特征去噪

合成孔径雷达(SAR)图像具有丰富的纹理信息,这些纹理信息能反映地物空间结构关系。当前纹理特征被广泛应用于SAR图像分类和SAR图像分割中。受成像因素影响,直接采用从SAR图像中提取的纹理特征效果不够好。为避免传统先滤波再提取纹理特征的方法对纹理、边缘信息造成损失,提出了一种先提取SAR图像纹理特征,再利用Robust PCA方法对纹理特征去噪的新方法,最后采用Kmeans聚类方法检验RPCA处理后的纹理特征表达效果。实验结果表明该方法能将聚类正确率从82%提高到84%。     

Time-varying ARMA stable process estimation using sequential Monte Carlo

Various time series data in applications ranging from telecommunications to financial analysis and from geophysical signals to biological signals exhibit non-stationary and non-Gaussian characteristics. α-Stable distributions have been popular models for data with impulsive and non-symmetric characteristics. In this work, we present time-varying autoregressive moving-average α-stable processes as a potential model for a wide range of data, and we propose a method for tracking the time-varying parameters of the process with α-stable distribution. The technique is based on sequential Monte Carlo, which has assumed a wide popularity in various applications where the data or the system is non-stationary and non-Gaussian.     

InSAR地面场景仿真方法研究

本文利用随机分形理论、小平面单元模型以及地面杂乱回波模型针对自然地表场景提出了一种InSAR系统地面场景仿真方法.根据实地勘测的Alps山脉DEM数据进行仿真,理论分析和仿真结果表明该仿真算法能够更加有效地重构不同地表的DEM数据以及仿真得到不同系统参数下的干涉SAR数据,为干涉数据处理算法提供具有先验知识的数据源,并对InSAR系统设计方案的优化设计具有重要的意义.     

干涉合成孔径雷达系统的最优基线

本文从干涉合成孔径雷达（ＩＮＳＡＲ）的系统原理出发,深入地了影响ＩＮＳＡＲ系统特性的干涉基线。基于形成干涉图像的ＳＡＲ信号之间的频率偏移理论,本文引入干涉合成孔径雷达系统的信号带宽、信噪比及距离向分辨力的概念。根据所提出的概念,得到干涉系统的最优基线,仿真计算结果与实际系统中所采用的系统基线基本吻合。     

利用灰度和纹理特征的SAR图像分类研究

多类别多特征量情况下的合成孔径雷达(SAR)图像的目标分类是一个难以解决的问题.从灰度和纹理模型出发,提出了综合利用灰度和纹理特征的目标分类方法.均值和方差是灰度模型中重要的特征统计量,而能量、熵、对比度、局部相似性和相关性是纹理模型中重要的特征统计量.灰度和纹理特征能确切地描述SAR图像中的目标.通过构造特征向量,定义向量之间的距离,并按照最小距离方法进行目标分类.以一定大小的窗口读入样本,提高了算法的运行速度和抗噪能力.理论上,窗口越大,特征向量值越接近真实值.窗口越小,边缘的分类精度越高.实验表明该方法较好地处理了多类别多特征量情况下的SAR图像分类问题,分类结果是有效的,这为SAR图像目标分类提供了一条简单可行的途径.     

SAR image data compression using a tree-structured wavelettransform

SAR image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. The authors propose a compression scheme driven by texture analysis, homogeneity mapping and speckle noise reduction within the wavelet framework. The image compressibility and interpretability are improved by incorporating speckle reduction into the compression scheme. The authors begin with the classical set partitioning in hierarchical trees (SPIHT) wavelet compression scheme, and modify it to control the amount of speckle reduction, applying different encoding schemes to homogeneous and nonhomogeneous areas of the scene. The results compare favorably with the conventional SPIHT wavelet and the JPEG compression methods     

SAR ocean image decomposition using the Gabor expansion

Demonstrates the utility of the Gabor expansion as a new tool in geophysical research. The Gabor expansion provides good time-frequency (or space-wavenumber) localization and is ideally suited to represent nonstationary processes. The properties of this tool are demonstrated by expanding an FM-chirp waveform, and azimuth cuts taken from two different SAR ocean images. The effects of filtering in Gabor phase space are also investigated     

Approaching MIMO capacity using bitwise Markov Chain Monte Carlo detection

This paper examines near capacity performance of Markov Chain Monte Carlo (MCMC) detectors for multipleinput and multiple-output (MIMO) channels. The proposed MCMC detector (Log-MAP-tb b-MCMC) operates in a strictly bit-wise fashion and adopts Log-MAP algorithm with table look-up. When concatenated with an optimized low-density parity-check (LDPC) code, Log-MAP-tb b-MCMC can operate within 1.2-1.8 dB of the capacity of MIMO systems with 8 transmit/receive antennas at spectral efficiencies up to n = 24 bits/channel use (b/ch). This result improves upon best performance achieved by turbo coded systems using list sphere decoding (LSD) detector by 2.3-3.8 dB, leading to nearly 50% reduction in the capacity gap. Detailed comparisons of the Log- MAP-tb b-MCMC with LSD based detectors demonstrate that MCMC detector is indeed the detector of choice for achieving channel capacity both in terms of performance and complexity.     

Quantifying spatial localization of optical mapping using Monte Carlo simulations

Optical mapping techniques used to study spatial distributions of cardiac activity can be divided into two categories. 1) Broad-field excitation method, in which hearts stained with voltage or calcium sensitive dyes are illuminated with broad-field excitation light and fluorescence is collected by image or photodiode arrays. 2) Laser scanning method, in which illumination uses a scanning laser and fluorescence is collected with a photomultiplier tube. The spatial localization of the fluorescence signal for these two methods is unknown and may depend upon light absorption and scattering at both excitation and emission wavelengths. We measured the absorption coefficients (micro a), scattering coefficients (micro s), and scattering anisotropy coefficients (g) at representative excitation and emission wavelengths in rabbit heart tissue stained with di-4-ANEPPS or co-stained with both Rh237 and Oregon Green 488 BAPTA 1. Monte Carlo models were then used to simulate absorption and scattering of excitation light and fluorescence emission light for both broad-field and laser methods in three-dimensional tissue. Contributions of local emissions throughout the tissue to fluorescence collected from the tissue surface were determined for both methods. Our results show that spatial localization depends on the light absorption and scattering in tissue and on the optical mapping method that is used. A tissue region larger than the laser beam or collecting area of the array element contributes to the optical recordings.     

Weighted ensemble Monte Carlo

A new approach to the Monte Carlo method for the solution of electron transport problems in semiconductors is presented and applied to some special cases of interest. The method is based on an iterative expansion of the Boltzmann equation in Chambers form and yields a simulative algorithm where the scattering events are chosen with arbitrary probabilities. In this way it is possible to analyse in great details regions of real and/or momentum space that would rarely be visited in a standard simulation. Applications are shown to the problems of high energy tails, of the surmounting of a high energy barrier, and of the low-field contact region.     

Transport study in Si-silicide-Si transistors using a Monte Carlo technique

The Monte Carlo technique has been used for the study of electron motion in a proposed Si-silicide-Si transistor (SST). The transmission coefficient and the transit time are calculated as functions of lattice temperature, initial energy of electrons coming from the emitter, and the applied base-collector bias. The results show that a maximum transmission coefficient for electrons occurs when the initial energy exceeds the maximum energy barrier of the base-collector junction by about 0.1 eV, and the transit time decreases as the applied base-collector junction bias increases and as the temperature decreases. Space charge effects caused by operating at high current densities are shown to reduce slightly the transmission coefficient.     

Stochastic nonlinear image restoration using the wavelet transform

The dominant methodology for image restoration is to stabilize the problem by including a roughness penalty in addition to faithfulness to the data. Among various choices, concave stabilizers stand out for their boundary detection capabilities, but the resulting cost function to be minimized is generally multimodal. Although simulated annealing is theoretically optimal to take up this challenge, standard stochastic algorithms suffer from two drawbacks: i) practical convergence difficulties are encountered with second-order prior models and ii) it remains computationally demanding to favor the formation of smooth contour lines by taking the discontinuity field explicitly into account. This work shows that both weaknesses can be overcome in a multiresolution framework by means of the 2-D discrete wavelet transform (DWT). We first propose to improve convergence toward global minima by single-site updating on the wavelet domain. For this purpose, a new restricted DWT space is introduced and a theoretically sound updating mechanism is constructed on this subspace. Next, we suggest to incorporate the smoothness of the discontinuity field via an additional penalty term defined on the high frequency subbands. The resulting increase in complexity is small and the approach requires the specification of a unique extra parameter for which an explicit selection formula is derived.