基于EM和BIC的直方图拟合方法应用于遥感变化检测阈值确定

利用遥感图像进行变化检测时,确定"差异图像"上各变化类型的阈值非常关键.本文引入图像直方图拟合方法来确定变化阈值.首先通过基于变化向量分析方法,得到变化强度图像,然后假设该变化强度图像中的像元值符合混合高斯分布模型,利用期望最大(EM)算法和贝叶斯信息准则(BIC)求出最佳的混合高斯分布模型,拟合此时的图像直方图,最后利用贝叶斯判别准则确定出各变化类型的变化阈值.试验证明,这种方法是一种较为有效的自动确定变化阈值的方法.     

集聚型空间点模式结构信息提取研究

空间点模式是一个2维离散点集,点集中的每一个元素代表地球表面一个点状目标的空间位置。当2维离散点集具有集聚特征时,称其为集聚型空间点模式,它与空间聚类、制图综合和空间分析的许多具体应用紧密相关。如何提取集聚型空间点模式的结构信息(集聚子群的个数和对应的集聚中心)是其中尚未彻底解决的问题。作者以几何概率为理论基础,提出测度正方形区域内2维离散点集分布特征的H函数并推导其解析表达式,运用H函数设计和实现了集聚型2维离散点集结构信息提取的通用算法。利用该算法处理一个由居民地坐标数据得到的具有集聚特征的空间点模式,提取出其结构信息并进行可视表达。分别以该空间点模式中的各离散点为顶点和发生元生成Delaunay三角网和Voronoi图,在Delaunay三角网中保留面积最小的前1/10、前1/100三角形的顶点,在Voronoi图中保留面积最小的前1/10、前1/100邻近多边形的发生元,将可视表达的点集结构信息分别与依据Delaunay三角网和Voronoi图得到的结果进行对比分析。结果表明,运用H函数能够有效地提取出集聚型空间点模式的全局性结构信息,而Delaunay三角网和Voronoi图虽然能够反映其局部密度,但在提取全局结构信息时存在局限性。     

Microwave response of seasonal snow-cover measured by using a ground-based radiometer at 6.93 and 18.7 ghz frequencies and at dual polarization

Snow cover is an important variable for climatic and hydrologic models due to its effects on surface albedo, energy, and moisture budgets. Passive microwave sensors can be used to monitor temporal and spatial variations in large-scale snow cover parameters, avoiding problems of cloud cover and polar nights. In the present study, brightness temperature values were estimated (using calibration curves) for moist snow on natural and blackbody/metal surface. T_B response on snow depth, density, SWE and angular variation from nadir were measured and found that T_B decreases with increase of snow depth and with increase of angle from nadir. Empirical relations were used to estimate emissivity, dielectric constant and dielectric loss factor. It was observed that emissivity decreases with the increase of dielectric constant. The dielectric constant and dielectric loss factor both increases with the increase of density. Experiments were performed during winter of year 2005 at Dhundi and Solang (H.P.), India, using ground based passive microwave radiometer having 6.9 and 18.7 GHz antenna frequencies at dual polarization.     

Directional effects on NDVI and LAI retrievals from MODIS: A case study in Brazil with soybean

The Moderate Resolution Imaging Spectroradiometer (MODIS) is largely used to estimate Leaf Area Index (LAI) using radiative transfer modeling (the “main” algorithm). When this algorithm fails for a pixel, which frequently occurs over Brazilian soybean areas, an empirical model (the “backup” algorithm) based on the relationship between the Normalized Difference Vegetation Index (NDVI) and LAI is utilized. The objective of this study is to evaluate directional effects on NDVI and subsequent LAI estimates using global (biome 3) and local empirical models, as a function of the soybean development in two growing seasons (2004–2005 and 2005–2006). The local model was derived from the pixels that had LAI values retrieved from the main algorithm. In order to keep the reproductive stage for a given cultivar as a constant factor while varying the viewing geometry, pairs of MODIS images acquired in close dates from opposite directions (backscattering and forward scattering) were selected. Linear regression relationships between the NDVI values calculated from these two directions were evaluated for different view angles (0–25°; 25–45°; 45–60°) and development stages (<45; 45–90; >90 days after planting). Impacts on LAI retrievals were analyzed. Results showed higher reflectance values in backscattering direction due to the predominance of sunlit soybean canopy components towards the sensor and higher NDVI values in forward scattering direction due to stronger shadow effects in the red waveband. NDVI differences between the two directions were statistically significant for view angles larger than 25°. The main algorithm for LAI estimation failed in the two growing seasons with gradual crop development. As a result, up to 94% of the pixels had LAI values calculated from the backup algorithm at the peak of canopy closure. Most of the pixels selected to compose the 8-day MODIS LAI product came from the forward scattering view because it displayed larger LAI values than the backscattering. Directional effects on the subsequent LAI retrievals were stronger at the peak of the soybean development (NDVI values between 0.70 and 0.85). When the global empirical model was used, LAI differences up to 3.2 for consecutive days and opposite viewing directions were observed. Such differences were reduced to values up to 1.5 with the local model. Because of the predominance of LAI retrievals from the MODIS backup algorithm during the Brazilian soybean development, care is necessary if one considers using these data in agronomic growing/yield models.     

顾及不确定性影响的变形概率预报法

针对变形预报的不确定性,以MCMC算法和贝叶斯预测理论为基础,提出了变形概率预报方法,该方法以概率分布的形式描述变形预报的不确定性,通过概率规则实现预报的递推过程。利用宁杭高速路基沉降数据进行数值计算,定量分析了预报值及其可靠性区间等信息,并与最小二乘估计、免疫算法的预报结果进行比较,结果表明了该方法的有效性和可行性。     

Soil Moisture Estimation in a Vegetated Area Using Combination of AIRSAR and Landsat5-TM Images

High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively.     

一种基于SVM后验概率的MRF分割方法

提出了一种基于SVM后验概率的MRF分割方法,将支持向量机的后验概率应用于Markov随机场方法中,通过贝叶斯公式将对样本条件概率的估计转换为后验概率估计,再通过对SVM决策函数输出的映射来产生后验概率,并将SVM估计的后验概率信息带入MRF模型实现分割,从而完成了一种新的Markov随机场模型的分割方法.实验结果表明,采用此方法分割纹理图像可以获得较好的分割结果.     

一种新的SAR图像船只检测方法

提出一种新的基于恒虚警率(CFARConstantFalseAlarmRate)技术,确定SAR图像中检测船只整体阈值的方法。该方法采用高斯分布(正态分布)作为SAR图像灰度的概率密度函数,由CFAR技术直接导出用于检测船只整体阈值的计算公式,用记数滤波器滤波去除虚警。该算法避免了复杂公式迭代和求解形状参数计算过程,也避免了用二分法寻找阈值的循环解算过程,提高了检测速度。使用XSAR和ERSSAR图像对该算法进行检验,并与其它算法进行比较,结果显示所提出的算法在检测精度和检测速度上都有明显的改进。     

Satellite Doppler solutions in terms of a single parameter

By an appropriate combination of the integrated doppler counts for a motionless ground station over two consecutive arcs, of a satellite path, it is possible to obtain a linear mathematical model relating the coordinates of the ground station to the observations. In this mathematical model, the involvement of the fourth unknown of the problem—the frequency off-set parameter, is, however, not linear. By application of the least squares technique, the solutions for the coordinates are obtained as analytical functions of the frequency off-set, parameter only. These, in turn, reduce the basic formula for the doppler count to be an implicit function of the same single variable. The value of the variable which provides the best fit of this function with the observed doppler counts, minimizing the sum of squares of the deviations for all involved pairs of satellite positions is the best value for the unknown frequency off-set parameter and an iterative technique is devised to compute this value. The desired values for the coordinates of the ground station can then be obtained by substitution of the best value of the frequency off-set parameter into the corresponding formula, and correcting for reducing the effects of the random noise in the observed dopple data.     

利用STLN和InSAR数据反演2008年青海大柴旦Mw 6.3级地震断层参数

提出了将总体最小二乘方法应用于联合平差的模型,推导了附有相对权比的总体最小二乘联合平差方法。采用了多种方案来确定相对权比的大小。以参数估值与真值的差值范数作为评价指标,分析比较了单一数据总体最小二乘平差和两类数据总体最小二乘联合平差的模拟算例;通过给各类数据加入不同大小的随机噪声,分析了判别函数最小化法中随机噪声大小对确定相对权比的影响。模拟算例表明,平差结果的质量与相对权比的选取有关;当先验信息准确时,验前单位权方差法的结果最好,而当先验信息不准确时,判别函数为相似文献   

Assessing the Veracity of Methods for Extracting Place Semantics from Flickr Tags

The volume and potential value of user generated content (UGC) is ever growing. Multiply sourced, its value is greatly increased by the inclusion of metadata that adequately and accurately describes that content – particularly if such data are to be integrated with more formal data sets. Typically, digital photography is tagged with location and attribute information that variously describe the location, events or objects in the image. Often inconsistent and incomplete, these attributes reflect concepts at a range of geographic scales. From a spatial data integration perspective, the information relating to “place” is of primary interest. The challenge therefore is in selecting the most appropriate tags that best describe the geography of the image. This article presents a methodology based on an information retrieval technique that separates out “place related tags” from the remainder of the tags. Different scales of geography are identified by varying the size of the sampling area within which the imagery falls. This is applied in the context of urban environments, using Flickr imagery. Empirical analysis is then used to assess the correctness of the chosen tags (i.e. whether the tag correctly describes the geographic region in which the image was taken). Logistic regression and Bayesian inference are used to attach a probability value to each place tag. The high correlation values achieved indicate that this methodology can be used to automatically select place tags for any urban region and thus hierarchically structure UGC in order that it can be semantically integrated with other data sources.     

一种基于证据理论的多极化SAR图像分割方法

本文提出了一种基于证据理论的融合像素信息和上下文信息的多极化合成孔径雷达(SAR)图像分割方法.D-S证据理论是一种不确定性推理方法.基于D-S的SAR影像分割方法将像素信息和上下文信息看作两类证据,先对高度平滑后的影像作初始的过分割,然后基于D-S理论对初始分割图斑的边界进行迭代修正,最后再融合两类证据对初始分割的图...     

基于B/S的土地利用总体规划信息系统研究

探讨建立基于B/S(浏览器/服务器)模式的土地利用总体规划信息系统。对系统的功能和系统实现的几个关键技术进行了详细说明,包括数据库连接技术、MSchart控件的使用、SQL语句查询的算法设计。此系统是一种基于Internet技术的信息系统[3],用户可以通过浏览器来浏览和查询网络上的土地信息。     

Combination of Spectral and Textural Features in the MSG Satellite Remote Sensing Images for Classifying Rainy Area into Different Classes

The rainfall intensity classification technique using spectral and textural features from MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared) satellite data is proposed in this paper. The study is carried out over north of Algeria. The developed method is based on the artificial neural multilayer perceptron network (MLP). Two MLP algorithms are used: the MLP-S based only on spectral parameters and the MLP-ST that use both spectral and textural features. The MLP model is created with three layers (input, hidden, and output) that consist of 6 output neurons in the output layer that represent the 6 rain intensities classes: very high, moderate to high, moderate, light to moderate, light and no rain and 10 spectral input neurons for the MLP-S and 15 input neurons for MLP-ST, which as ten spectral features that were calculated from MSG thermal infrared brilliance temperature and brilliance temperature difference and as five textural features, and The rainfall intensity areas classified by the proposed technique are validated against ground-based radar data. The rainfall rates used in the training set are derived from Setif radar measurements (Algeria). The results obtained after applying this method show that the introduction of textural parameters as additional information works in improving the classification of different rainfall intensities pixels in the MSG/SEVIRI imagery compared to the techniques based only on spectral information. These results are compared with results obtained with the probability of rainfall intensity (PRI). This comparison revealed a clear outperformance of the MLP algorithms over the PRI algorithms. Best results are provided by the MLP-ST algorithm. The combination of spectral and textural features in the MSG–SEVIRI imagery is important and for the classification of the rainfall intensities to different classes.     

超谱遥感图像快速聚类无损压缩算法

K-means聚类要求每个像素要和所有聚类中心求欧氏距离，当聚类数很多时，这是一个相当耗时的工作。改进的K—meam聚类算法根据历史聚类结果进行初始类分割，即节约初始聚类时间，又能使历史聚类过程中形成的类间稳定关系得以保持；类内像素只和相邻的聚类中心计算距离进行聚类，随着算法的迭代进行，大量类的状态基本固定，使得聚类速度不断加快。基于改进K-means聚类的无损压缩算法具有充分利用历史聚类成果和收敛速度快的特点，通过提高类内像素冗余度，最大限度消除谱间冗余和空间冗余。采用多次聚类压缩的结果预测最佳聚类数的方法，可实现最小熵无损压缩。通过和DPCM算法概率模型的熵值比较及实验数据的分析，验证了基于聚类无损压缩效率比不聚类无损压缩效果更优。     

MapX支持下道路网络拓扑结构构建方法

对于一个实用的车载导航系统,最优路径规划是其必备的十分重要的功能,而道路网络拓扑结构的构建又是最优路径规划的基础.对基于MapInfo的道路网络拓扑结构的构建算法进行了初步的研究,设计了表达道路网络拓扑结构的数据结构,并在MapX控件支持下在VC 6.0环境下完成了该算法的实现.     

Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.     

曲波变换的高光谱遥感图像融合方法在土地利用调查中的应用

对两个有关土地利用状况调查区域的高光谱和Quickbird数据进行了数百次算法对比实验,最终确定Sym4和Db2是曲波变换中的最佳小波函数,提出两种基于曲波变换的高光谱遥感图像融合方法,即加权法和选择法,获得了更精确的高光谱遥感融合影像.在一个研究区域,通过对高光谱数据的子带分解、脊波变换、一定融合以及它们的逆变换等曲波变换过程,表明融合的高光谱遥感影像能提供许多有利于土地类型划分和影像识别的精确七地信息.在另一个研究区域,除进行曲波变换外,还通过二次多项式计算实现了高光谱与Quickbird数据遥感影像匹配,最终从高光谱与Quickbird数据获得的遥感融合影像经日视及信息熵、相关系数等评价表明,曲波变换融合图像优于小波变换融合图像,与Brovey融合图像相近,但比PCA融合图像稍差.客观和主观评价证实,曲波变换的遥感融合方法能为高光谱遥感图像在土地利用状况调查提供更多更准确的信息.     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

GIS中最佳遍历分析算法与实现

首先介绍了地理信息系统（GIS）网络分析的发展概况;其次在已有GIS最佳遍历分析存在问题的基础上,提出了一种最佳多路遍历分析算法。并在ArcEngine环境下,以某县交通网为例,利用C#.net及ArcEngine二次开发类库实现了其最佳多路灾情巡视路线分析。算法适用范围广,可以推广到很多同类的地理网络问题。