单视数SAR图像Speckle滤波方法的研究

在分析单机数SAR影像的Speckle局部统计特性和现有的空间滤波算法的基础上,提出了一种新的Speckle滤波方法,该方法采用特殊的滤波窗口邻域划分方法,根据窗口相对标准差自适应地调整窗口尺寸和窗口内的滤波区域。该方法被用于多幅单视数ERS-1/2SAR图像去Speckle处理,并与以往的典型算法进行了比较,取得了较好的实验结果。     

全方向自适应动态窗口SAR斑点噪声抑制算法

有效地抑制或消除斑点噪声是SAR图像地学应用的前提，通过基于单视数SAR图像的Speckle统计特性和已发展的空间滤波算法分析。发展了一种改进的全方向动态窗口自适应SAR噪声滤波算法，该算法对处理的每一个像元可按图像边界细节划分为需要的全方向子窗口，利用相对标准差判断滤波窗口及子窗口内斑点噪声及边缘信息的存在情况，可实现滤波窗口大小动态调整和窗口内参加滤波像素的自适应选择，对ERS SAR SLC图像试验结果表明，该算法对单视数SAR图像具有较强的Speckle抑制能力。且可较好地保持图像的纹理边界细节信息，有一定的实用价值。     

结合相似块匹配及线性最小均方误差滤波器的全极化雷达影像去噪

针对全极化合成孔径雷达Pol SAR(Polarimetric Synthetic Aperture Radar)影像相干斑噪声严重的问题,提出了一种结合相似块匹配和线性最小均方误差原理的去噪方法。该方法首先在原始影像上实现相似块组的匹配,进而利用线性最小均方误差滤波器对影像块组进行滤波得到初始去噪结果;然后,同时利用原始影像和初始去噪影像的信息进行相似块组的重新匹配,并再次利用线性最小均方误差原理对重匹配影像块进行去噪,得到影像最终的去噪结果。利用模拟的Pol SAR影像和高分三号卫星Pol SAR影像进行了算法效果的验证。结果表明,去噪算法在显著抑制影像噪声水平的同时,也能较好地保持影像的边缘和极化特性等细节信息。     

一种基于相对标准差的SAR图像Speckle滤波方法

通过分析ＳＡＲ影像的Ｓｐｅｃｋｌｅ局部统计特性和现有的空间波波算法,提出了一种新的Ｓｐｅｃｋｌｅ滤波该方法以局部部相对标准差为主要依据,采用特殊的滤波窗口邻域划分方法,自适应地调整窗口尺寸和窗口内的滤波区域。该方法被用于多幅ＥＲＳ－１／２ＳＡＲ图像去Ｓｐｅｃｋｌｅ处理,并与以入的典型算法进行了比较实验,取得了令人满意的结果。     

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 fixed-size 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 single- and 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.     

Image fusion by spatially adaptive filtering using downscaling cokriging

The aim of this paper was to extend the method of downscaling cokriging for image fusion by making the method spatially adaptive in that the filter parameters (cokriging weights) can change across the image. The method can adapt itself to the usual statistical non-homogeneity (spatially variable mean, variance and correlation length) of a satellite sensor image that covers an area with different spatial patterns of geographical objects or different terrain types. The solution adopted was to estimate the models of covariances and cross-covariances (or semivariograms and cross-semivariograms) by the same procedure as described in Pardo-Iguzquiza et al. (2006) but with the method applied locally instead of globally. The correct implementation of this local estimation is the key for computational feasibility and prediction efficiency. Two parameters to be taken into account are the grid of locations on which a moving window is centred (local modelling is performed inside this window) and the size of this moving window. With respect to the latter parameter, there is a trade-off between a size small enough to make the procedure locally adaptive and large enough to produce reliable statistical estimates. The computational burden will impose limits to the distance between grid points on which the local moving window is centred. A case study with Landsat ETM+ images was used to show the implementation of the method and the result was evaluated using several statistics widely used for assessing the quality of a fused image, apart from its visual appearance.     

基于Freeman分解的极化SAR数据相干斑滤波

本文在Freeman分解理论基础上,提出一种极化SAR数据相干斑滤波算法。该算法基于Freeman散射模型对原始极化SAR数据进行分解,以获得像素的散射类型和总功率值;在滤波窗口内选取与中心像素散射类型相同,总功率值接近的像素构成滤波同质区;并根据同质区的局部统计特性,应用线性最小均方滤波器进行滤波处理。试验表明,该算法在有效去除相干斑的同时,对极化和边缘等细节信息也有较好的保持效果。     

任意方向对称差值核的SAR图像边缘提取算法

由于现有边缘检测方法多存在对图像噪声的敏感性以及检测边缘的有限方向性等问题,因此在斑点噪声的影响下难以有效检测SAR图像中具有任意方向性且强度相近的区域间边缘。本文提出了一种任意方向对称窗口的SAR图像边缘提取算法。首先,以任意像素在图像中的格点位置为对称中心构建任意方向上的对称窗口;定义各窗口内像素到中心像素距离的核函数;以像素核函数为权重分别计算两对称窗口内像素光谱测度的加权平均值,并定义两者之差的绝对值为该方向上该像素隶属边缘的强度;选取所有方向中最大边缘强度作为边缘像素的判别标准。为去除错误提取的边缘像素,设计基于滤波操作的后处理过程。对模拟图像提取结果的定性及定量分析表明提出算法可在一定程度上克服噪声影响,较为准确地提取任意方向的边缘。     

道路综合特征下高分辨率遥感影像的提取

针对在高分辨率遥感影像中如何提高道路信息提取的准确度和信息量这一问题,通过对影像光谱和纹理特征的分析,将影像特征按照2种光谱特征和3种纹理特征进行分类,进而改善传统的图像分割方法,选择灰度级数和像素对的相对方向、距离和窗口大小作为参数,再通过灰度共生矩阵运算获取影像的纹理信息,通过对这些纹理特征的综合比较分析,最后确定角二阶矩、熵和对比度作为道路纹理特征统计量;再通过对图像像元分析比较,将图像像元标准差和灰度均值作为道路信息提取的光谱特征;在对道路综合特征分析基础上,再通过对遥感图像几何特征分析,最后利用数学形态学的开运算、闭运算、腐蚀、细化等模型算法对遥感图像进行精细化处理,得到道路提取较好的结果。该方法可用于复杂路况的道路信息提取。     

遥感图像纹理分析方法

本文针对遥感图像纹理特征,定义纹理是“图像像元亮度值空间变化率的一种量度”.据此定义,提出纹理强度和纹理密度两个新参量,定量地描述遥感图像的纹理特性。这两个参量反映了图像中相邻象元亮度值的差别及图象中象元亮度值变化的频数。所设计的相应提取方法包括:强度和法、绝对差法与最大、最小值法等三种纹理强度提取方法,以及两种纹理密度提取方法:一阶纹理密度法与二阶纹理密度法。同时,讨论了局部移动窗口大小的选择原则。本文不仅设计了从遥感图像亮度值的绝对变化提取纹理特征的方法,并进而提出相对梯度概念,从象元亮度值的相对变化描述图象的纹理特征;由相对梯度法生成的纹理图像,不仅可突出遥感图像的空间信息特征,并保留了原图像的部份波谱信息,原图像中的甚亮区和暗区、甚暗区（如山地背阴坡）的纹理结构得以清晰地显现,效果独特。     

倾斜立体影像的Shi-Tomasi特征匹配

针对倾斜影像畸变大的现象,提出一种适用于倾斜立体影像匹配的方法。首先采用基于SIFT算法的匹配点对消除影像间的射影失真;然后提取Shi-Tomasi特征并采用多窗口的归一化互相关系数(NCC)计算相似性测度,获得稳定的匹配点对;最后使用两组倾斜影像将该算法同SIFT算法和ASIFT算法进行综合对比分析。实验结果表明,所提算法在正确匹配点对数量、匹配点对分布及匹配精度上均优于其他两种算法。     

TM遥感图像FLAASH大气校正异常值的改正

针对经过ENVI的FLAASH模型大气校正后的反射率遥感图像中,经常存在异常值,即负值和大于100的高值,在水体分布众多的图像中尤其明显的问题,以Landsat的TM图像的校正结果为例,设计了改正算法,即对于异常高值采用阈值法进行改正,对于异常负值采用窗口搜索最小正值法进行改正。使用统计方法和NDVI植被指数对改正前后图像进行了对比。与改正前的图像相比,改正后图像进行计算的结果合理,表明算法是可行的和有效的。所提出的改正算法能够行之有效地改正图像中的异常值,为之后的遥感信息提取提供了良好的数据基础,具有一定的研究意义和应用价值。     

Making more out of pixel-level change information: using a neighbourhood approach to improve land change characterization across large and heterogeneous areas

The paper demonstrates two issues; (i) how a ‘moving window approach’, that translates pixel level detected changes to landscape level, can be implemented; (ii) how the approach can overcome the limitations of pixel level change information to characterize change over large areas. First we detected changes from two periods (1986 and 2010) of LULC maps. On the pixel-based changes, we ran focal statistics summation operator separately for selected window sizes (1–10 km). Further, we assessed effect of scale in depicting the pattern and amount of change. The approach is found useful to overcome major shortfalls of pixel-based change characterization. However, varying scale of analysis provide varying amount of change and differently represent change patterns. Thus, implementing the approach over complex and large areas requires multi-scale approach. Subdividing complex and large areas into homogeneous zones can help to implement the multi-scale approach and facilitate the selection of appropriate scale of analysis.     

A piecewise based high-precision registration of InSAR data

In Synthetic Aperture Radar (SAR) interferometry, the precise co-registration is a key problem to obtain the fine interferogram. Theoretically, 1/10 pixel accuracy is essential. Once the control points are determined, the relative rectification (pixel re-sampling) is also the important procedure. In this paper, a piecewise transformation algorithm based on Delaunay Tessellation algorithm is developed. The strategy is to “anchor” geographically all control points and transform the imagery on the basis of triangle by triangle. The piecewise algorithm, the accuracy evaluation and the preliminary results are described. The experiment result shows that the piecewise transformation out-performes the traditional polynomial transformation.     

A piecewise based high-precision registration of InSAR data

In Synthetic Aperture Radar (SAR) interferometry, the precise co-registration is a key problem to obtain the fine interferogram. Theoretically, 1/10 pixel accuracy is essential. Once the control points are determined, the relative rectification (pixel re-sampling)is also the important procedure. In this paper,a piecewise transformation algorithm based on Delaunay Tessellation algorithm is developed. The strategy is to "anchor" geographically all control points and transform the imagery on the basis of triangle by triangle. The piecewise algorithm, the accuracy evaluation and the preliminary results are described. The experiment result shows that the piecewise transformation out-performes the traditional polynomial transformation.     

SAR图像保留边缘多窗口相干斑滤除方法

在处理合成孔径雷达（ＳＡＲ）图像中,精确地估计图像的局部统计特性和尽可能多地滤除相干斑都需要较大的窗口尺寸,而这与图像边缘和纹理结构的检测需要较小的窗口尺寸相矛盾。一般滤波器都是二者窗口尺寸的均衡,这必然导致二者都不能取得很好的效果。本文提出的新方法有效的解决了这一矛盾。此方法使处理后的ＳＡＲ图像既保存了边缘和纹理结构,避免了以往滤波器中使边缘变模糊的问题,又有效地滤除了相干斑噪声。     

Local Contrast Based Adaptive SAR Speckle Filter

In this paper, we propose an adaptive filtering technique for Synthetic Aperture Radar (SAR) images. A new windowing technique is introduced where the total window is divided into five equal sized overlapping sub-windows. The pixel to be filtered is a part of each of these sub-windows. A weighted mean of all sub-windows is computed for the pixel under consideration. The weights are accounted from a measure of heterogeneity calculated for each sub-windows. The filter is able to adapt automatically and adjust the speckle suppression strength based on local statistics. This allows the filter to preserve edges while strongly suppressing speckle over homogeneous areas. The proposed filter was compared with some well known SAR filtering techniques in terms of speckle suppression and edge preservation ability. Several experiments were performed on datasets acquired from both air-borne and space-borne SAR platforms. Some well known indices were used for quantitative comparison with other filters. Among the filters compared, the proposed filter shows good speckle suppression ability while still exhibiting reasonable edge preservation ability.     

用遗传算法确定马尔柯夫随机场的最佳参数

对现有马尔柯夫随机场 (MRF)参数的解求方法作了详细分析。针对现有方法存在的缺点 ,提出基于遗传算法的MRF参数解求方法 ;分析讨论了求解的稳定性 ;并利用解的稳定性作为纹理分类的判据 ,作了航空影像纹理的分类实验。实验结果表明 ,求解的方法是正确的。用解的稳定性作为判据的纹理分类效果 ,通过对比实验证明是有效的。     

A Neural Network Landuse Classifier for SAR Images Using Textural and Fractal Information

Abstract

Artificial neural networks (ANN) have recently been popularly used in image classification. Input features to most ANNs are extracted based on a one class per pixel basis. This requires a large number of training samples and thus a slow training rate. In this paper, we describe the use of a windowing technique to extract textural features such as average intensity, second moment of intensity histogram and fractal surface dimension from an image. This method of image characterization reduces the number of training samples efficiently, yet retains a reasonable overall classification accuracy. The ANN is trained based on the back‐error propagation algorithm. The method is applied for landuse classification of Synthetic Aperture Radar (SAR) images. An example is given for a site in Kedah State, Malaysia. The SAR images (HH,HV,VV) were taken by the Canadian Centre for Remote Sensing (CCRS) CV‐580 airborne C‐band SAR system in November 1993 during their GlobeSAR mission in Malaysia. These multi‐polarization SAR images are co‐registered with a Landsat Thematic Mapper (TM) channel 5 image from same area. An overall classification accuracy of about 86.95% is achieved using windowing technique, as compared to 68.22% based on one class per pixel approach. This shows that through fractal and textural information, the windowing technique when applied in an ANN classifier has a great potential in remote sensing applications.     

星载ASR图像的两种实用比R—D定位模型及其精度比较

以加拿大Radarsat卫星为例，在简要介绍了SAR图像的产品格式及其多级别，多类型的特点并比较了有关遥感图像的几种定位方法之后，指出距离－多普勒（R－D）原理用于SAR图像像素的绝对定位模型的优点，在分析了直接坐标变换所遇到的困难后，提出了两种基于R－D原理的实用化定位方法，并对算法进行了验证，作定位精度作了比较分析，试验结果表明，相对位置法所取得的定位精度达到了目前国际上有关这一领域的研究所取得的精度，效果良好，迭代法所取得的定位精度也在加拿大RSI公司技术文档的规定范围内，具有一定的实用性。