近45 a东北地区春季降水异常的气候特征

利用1959-2003年我国东北地区93站春季降水资料，将降水场分成5个区域，并在此基础之上分析了春季降水的时空变化特征，发现：降水量年际变化及长期趋势有明显的区域差异，呈东多西少的分布特征；西部是旱涝易发生区，近45a来降水量略有增多；降水量的周期振荡存在明显的区域差异。     

航空影像分割的最小二乘支持向量机方法

将最小支持向量机LS-SVM用于航空影像的分割,讨论了不同核函数对分割结果的影响和稀疏化处理对决策函数的影响。试验表明了LS-SVM方法用于航空影像分割的可行性。     

基于PCM改进算法的遥感混合像元模拟分析

混合像元的存在是影响遥感图像分类精度的主要原因,模糊分类是进行混合像元分解的重要方法,其效果的好坏取决于各像元分类后对各类别的隶属度值能否准确地反映像元的类别组成。当非监督分类中的聚类数目与实际类别数目不符,或者监督分类中训练样本存在未训练类别时,常用的模糊c-均值(FCM)方法的效果将大大降低,而可能性c-均值(PCM)方法则可以解决这个问题。该文提出了基于PCM算法的遥感图像混合像元分解方法,并用监督分类方法实例说明PCM方法的优越性。     

DMC＋4小卫星在国际灾害监测中的应用与评价

针对国际灾害监测星座应用技术和中国各种自然灾害的现状,着重研究了DMC(DisasterMonitoringConstellation)星座应用技术和DMC 4小卫星的数据特点,研究了利用小卫星星座对防灾、抗灾救灾的突出作用,研究了小卫星地面系统集成技术和星地一体化运营、管理和控制体系,以便进一步推动国内小卫星技术、遥感应用技术、卫星星座技术、天地一体化运管控技术和机制创新的发展。促进中国灾害监测星座的研制,最终实现对各种自然灾害的实时、动态监测。     

基于DMN的高光谱图像分割方法研究

简述高光谱遥感光谱特征体系(包括光谱曲线特征、光谱变换特征和光谱度量特征3个层面)。研究马尔科夫网的概念和方法,生成基于光谱角(SA)特征度量的DMN,并以DMN为证据对高光谱图像进行分割;研究和实验表明基于SA信息的马尔科夫网可以很好地综合高光谱数据空间特征与光谱特征间的关系,为进一步数据处理提供优化控制(其实质是概率神经网络)。最后提出未来应用和研究方向。     

国土资源行业“3S”技术集成研究及实用技术体系构建

本文面向国土资源信息化建设的实际需求,通过关键技术研究和技术集成,建立基于“3S”的国土资源数据获取、更新、管理、交换与应用的技术体系,通过应用示范研究,形成实用、可推广的技术流程和应用软件。研究结果表明,基于“3S”的技术体系,以其精确的空间定位、快速准确的数据获取、强大的数据管理能力,能够满足国土资源信息化建设的实际需要。     

测绘职业教育教学改革探讨

随着空间科学技术和计算机技术的发展，以“3S”技术为代表的测绘高新技术已普遍应用于测绘生产中。作为培养测绘实用型人才的测绘职业学校，测绘专业如何重新划分、测绘教育与教学内容如何改革，是测绘教育界普遍关注的问题，本文从教学的角度对有关问题进行了探讨。     

Geometry and accuracy of reflecting points in bistatic satellite altimetry

The analysis of the time and space distribution of specular (reflecting) points in bistatic altimetry between GPS and CHAMP satellites or SAC-C (taken as examples) is extended from Wagner and Klokočník (2003 J. Geod 77: 128–138). We demonstrate a significantly higher number and density of reflecting points in bistatic altimetry in comparison with traditional monostatic altimetry. After an outline of our older accuracy assessment for the vertical position of the reflecting point, we add a new independent derivation and compare both approaches. We account for orbit errors of both the transmitters (GPS) and receiver (CHAMP) satellites, and the measurement (delay) error. We found that the accuracy of the vertical position of the reflecting point decreases only slowly with increasing off-nadir angle and that the orbit errors must be accounted for if decimeter and better accuracy is required. In this paper, we do not study errors such as state of the ocean, technical parameters of the receiving system, and atmospheric corrections.     

Low-low satellite-to-satellite tracking: a comparison between analytical linear orbit perturbation theory and numerical integration

Low-low satellite-to-satellite tracking (ll-SST) range-rate observations have been predicted by two methods: one based on a linear perturbation theory in combination with the Hill equations, and one based on solving the equations of motion of two low-flying satellites by numerical integration. The two methods produce almost equivalent Fourier spectra of the range-rate observations after properly taking into account a few resonant terms. For a typical GRACE-type configuration, where the two satellites trail each other at a distance of 300 km at an altitude of 460 km, and in the presence of the EGM96 gravity field model, complete to spherical harmonic degree and order 70, the agreement between the Fourier spectra is about 1 mm/s compared to a root-mean-square (RMS) value of more than 220 mm/s for the range-rate signal. The discrepancy of 1 mm/s can be reduced significantly when not taking into account perturbations caused by the J₂ term. Excluding the J₂ term, the agreement between the two methods improves to 0.4 mm/s compared to a RMS value of 6 mm/s for the range-rate signal. These values are 0.01 and 2.3 mm/s when ignoring the spectrum for frequencies below two cycles per orbital revolution, reducing the discrepancy even further to about 0.5% of the signal. The selected linear perturbation theory is thus capable of modeling gravity field induced range-rate observations with very high precision for a large part of the spectrum.     

Outlier detection algorithms and their performance in GOCE gravity field processing

The satellite missions CHAMP, GRACE, and GOCE mark the beginning of a new era in gravity field determination and modeling. They provide unique models of the global stationary gravity field and its variation in time. Due to inevitable measurement errors, sophisticated pre-processing steps have to be applied before further use of the satellite measurements. In the framework of the GOCE mission, this includes outlier detection, absolute calibration and validation of the SGG (satellite gravity gradiometry) measurements, and removal of temporal effects. In general, outliers are defined as observations that appear to be inconsistent with the remainder of the data set. One goal is to evaluate the effect of additive, innovative and bulk outliers on the estimates of the spherical harmonic coefficients. It can be shown that even a small number of undetected outliers (<0.2 of all data points) can have an adverse effect on the coefficient estimates. Consequently, concepts for the identification and removal of outliers have to be developed. Novel outlier detection algorithms are derived and statistical methods are presented that may be used for this purpose. The methods aim at high outlier identification rates as well as small failure rates. A combined algorithm, based on wavelets and a statistical method, shows best performance with an identification rate of about 99%. To further reduce the influence of undetected outliers, an outlier detection algorithm is implemented inside the gravity field solver (the Quick-Look Gravity Field Analysis tool was used). This results in spherical harmonic coefficient estimates that are of similar quality to those obtained without outliers in the input data.