星载SAR图象斑点噪声消除方法效果的比较研究

星载（ＳＡＲ）图象一般都存在相干斑点噪声，严重干扰地物信息的提取与ＳＡＲ图象的应用效果，因此斑点噪声的消除问题引起微波遥感科技工作者的普遍关注，先后有多位学者提出各种不同的处理方法，其中最为常用的有均值平滑滤波、中值平滑滤波、Ｆｒｏｓｔ自适应滤波、Ｌｅｅ自适应滤波以及近期由Ｐ．Ｖ．ＮａｒａｓｉｍｈａＲａｏ等人提出的改良Ｋ－均值自适应滤波等方法。本文分析了这几种方法的数学基础，并选择了有代表性的实验区开展对比试验，结果表明：改良Ｋ－均值自适应滤波法效果最佳。     

速度光滑对反射地震信号的影响分析

光滑处理使得单界面成为非均匀薄层,界面反射转变为层反射.为了探讨光滑处理的影响,以平面波作为入射波场,首先利用过渡层反射系数推导了反射信号的理论公式,然后就非均匀薄层下反射系数的计算问题,给出了具体的实现算法,并通过与经典Epstein过渡层反射系数解析结果的对比说明了算法的精度.最后在单界面及其被光滑后界面的对比分析中,得出了几点重要结论：随着光滑次数的增加,反射信号的到时基本保持不变,而反射信号的主频与能量呈减少趋势,其中信号能量在低光滑次数的衰减速率明显大于高光滑次数.     

基于多分辨分析的滤波算法在SAR图像斑点噪声消除中的应用

合成孔径雷达 (SAR)图像一般都存在相干噪声 ,对地物信息的提取及 SAR图像应用的干扰非常严重 ,对此许多人提出了不少处理方法 ,并分析对比了多种滤波算法 ,认为改良 K -均值自适应滤波效果最佳。本文中分析了改良 K -均值自适应滤波法的数学基础的局限性 ,并采用了一种基于多分辨分析的滤波算法 ,该算法通过将噪声 (或较小的结构特征 )与较大的结构特征相分离 ,然后滤出噪声 (或较小的结构特征 ) ,从而达到消除斑点噪声的目的。实验结果证明 ,基于多分辨分析的滤波算法在消除 SAR图像的斑点噪声方面优于改良 K -均值自适应滤波法。     

气候突变的聚类分析

气候数据是时间序列,是一类有序样品．本文给出了一种新的有序样品谱系聚类算法,先对数据进行平滑拟合,然后用聚类分析的方法分析了北半球的气候资料,证实了1920年为气候突变点．同时也找到了较低的各个层次上的突变点．     

应用 TMS 32010 实现微机遥感图像快速处理

本文主要介绍一种利用TMS 32010数字信号处理器(DSP)和IBM-PC/XT微机构成的微机遥感图像快速处理系统。在此系统上,我们根据微波遥感图像的特点,采用TMS32010宏汇编语言研制出了一个遥感图像快速处理软件库。经过机载微波辐射计图像处理检验,采用TMS 32010语言编程在该系统上的图像处理速度要比采用FORTRAN语言编程在普通微机系统上的图像处理速度提高了100-500倍。此外,本文还提出一种新的平滑滤波算法。该算法滤除高斯白噪声的能力要优于中值滤波,同时还可以保护图像的边缘不被模糊。     

Evidence of 6 000-Year Periodicity in Reconstructed Sunspot Numbers

Reconstructed sunspot data are available that extend solar activity back to 11 360 years before the present. We have examined these data using Hurst analysis, a moving average filter, and Fourier analysis. All of the procedures indicate the presence of a long term (≈6 000 year) cycle not previously reported. A number of shorter cycles formerly identified in the literature by using Fourier analysis, Bayes methods, and maximum entropy methods were also detected in the reconstructed sunspot data.     

Exploring a valid model for the variogram of an isotropic spatial process

The variogram is one of the most important tools in the assessment of spatial variability and a crucial parameter for kriging. It is widely known that an estimator for the variogram cannot be used as its representator in some contexts because of its lack of conditional semi negative definiteness. Consequently, once the variogram is estimated, a valid family must be chosen to fit an appropriate model. Under isotropy, this selection is carried out by eye from the observation of the variogram estimated curve. In this paper, a statistical methodology is proposed to explore a valid model for the variogram. The statistic for this approach is based on quadratic forms depending on smoothed random variables which gather the underlying spatial variation. The distribution of the test statistic is approximated by a shifted chi-square distribution. A simulation study is also carried out to check the power and size of the test. Reference bands, as a complementary graphical tool, are calculated. An example from the literature is used to illustrate the methodologies presented.     

Assessing the optimal preprocessing steps of MODIS time series to map cropping systems in Mato Grosso,Brazil

The adoption of new cropping practices such as integrated Crop-Livestock systems (iCL) aims at improving the land use sustainability of the agricultural sector in the Brazilian Amazon. The emergence of such integrated systems, based on crop and pasture rotations over and within years, challenges the remote sensing community who needs to implement accurate and efficient methods to process satellite image time series (SITS) in order to come up with a monitoring protocol. These methods generally include a SITS preprocessing step which can be time consuming. The aim of this study is to assess the importance of preprocessing operations such as temporal smoothing and computation of phenological metrics on the mapping of main cropping systems (i.e. pasture, single cropping, double cropping and iCL), with a special emphasis on the iCL class. The study area is located in the state of Mato Grosso, an important producer of agriculture commodities located in the Southern Brazilian Amazon. SITS were composed of a set of 16-day composites of MODIS Vegetation Indices (MOD13Q1 product) covering a one year period between 2014 and 2015. Two widely used classifiers, i.e. Random Forest (RF) and Support Vector Machine (SVM), were tested using five data sets issued from a same SITS but with different preprocessing levels: (i) raw NDVI; (ii) raw NDVI + raw EVI; (iii) smoothed NDVI; (iv) NDVI-derived phenometrics; (v) raw NDVI + phenometrics. Both RF and SVM classification results showed that the “raw NDVI + raw EVI” data set achieved the highest performance (RF OA = 0.96, RF Kappa = 0.94, SVM OA = 0.95, SVM Kappa = 0.93), followed closely by the “raw NDVI” and the “raw NDVI + phenometrics” datasets. The “NDVI-derived phenometrics” alone achieved the lowest accuracies (RF OA = 0.58 and SVM OA = 0.66). Considering that the implementation of preprocessing steps is computationally expensive and does not provide significant gains in terms of classification accuracy, we recommend to use raw vegetation indices for mapping cropping practices in Mato Grosso, including the integrated Crop-Livestock systems.     

Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models

We discuss a new method for approximately decorrelating and non-isotropically filtering the monthly gravity fields provided by the gravity recovery and climate experiment (GRACE) twin-satellite mission. The procedure is more efficient than conventional Gaussian-type isotropic filters in reducing stripes and spurious patterns, while retaining the signal magnitudes. One of the problems that users of GRACE level 2 monthly gravity field solutions fight is the effect of increasing noise in higher frequencies. Simply truncating the spherical harmonic solution at low degrees causes the loss of a significant portion of signal, which is not an option if one is interested in geophysical phenomena on a scale of few hundred to few thousand km. The common approach is to filter the published solutions, that is to convolve them with an isotropic kernel that allows an interpretation as smoothed averaging. The downside of this approach is an amplitude bias and the fact that it neither accounts for the variable data density that increases towards the poles where the orbits converge nor for the anisotropic error correlation structure that the solutions exhibit. Here a relatively simple regularization procedure will be outlined, which allows one to take the latter two effects into account, on the basis of published level 2 products. This leads to a series of approximate decorrelation transformations applied to the monthly solutions, which enable a successive smoothing to reduce the noise in the higher frequencies. This smoothing effect may be used to generate solutions that behave, on average over all possible directions, very close to Gaussian-type filtered ones. The localizing and smoothing properties of our non-isotropic kernels are compared with Gaussian kernels in terms of the kernel variance and the resulting amplitude bias for a standard signal. Examples involving real GRACE level 2 fields as well as geophysical models are used to demonstrate the techniques. With the new method, we find that the characteristic striping pattern in the GRACE solutions are much more reduced than Gaussian-filtered solutions of comparable signal amplitude and root mean square.