不同分辨率遥感图像变化检测研究

变化检测是遥感图像应用的重要方面之一,变化检测一般采用同类传感器获得的图像.由于图像获取的条件有限,也可以用不同分辨率的传感器图像进行变化检测.本文讨论了不同分辨率遥感图像用于变化检测的方法与过程,并利用具体图像进行了变化检测.     

像素级和对象级高分辨率遥感图像变化检测分析

高分辨率遥感图像具有丰富的纹理信息,而像素级变化检测方法主要分析图像的光谱信息,导致将像素级变化检测方法用于高分辨率遥感图像具有一定的局限性。因此,本文提出了一种像素级与对象级相结合的高分辨率遥感图像变化检测方法,解决了像素级与对象级变化检测方法中存在的椒盐现象、误检等问题。首先,结合高分辨率遥感图像的多维特征,构建遥感图像变化检测模型;其次,利用随机森林分类器对图像进行分类,得到像素级变化检测结果;最后,将像素级变化检测结果与图像对象分割结果进行融合,得到图像变化区域和不变区域。试验结果表明,该算法具有较高的准确率和检测精度。     

基于多时相遥感图像灰度差值法的地表变化检测

为快速利用多时相遥感数据实现地表信息的变化检测,探讨了使用多时相遥感图像的灰度差值法来进行地表变化检测.计算差值图像的均值和标准差,建立其数学关系,确定变化阈值;通过差值运算后处理方法获得变化信息,并结合地表信息的光谱反射特性,进行城市地表信息的变化检测.     

遥感变化检测技术及其应用综述

从遥感变化检测前的准备工作和技术流程入手,对变化检测技术及应用现状作了简要介绍,综述了近些年来常用的几种遥感变化检测方法,即图像差值法、图像比值法、主成分分析法、植被指数法、分类后比较法。分析了遥感变化检测在国土资源、森林火灾、海洋、军事等方面发挥的重要作用。     

在GIS辅助下的地物变化检测

提出了将遥感图像和GIS相结合的变化检测和分析方法,并模拟地物环境变化对两幅不同时相的遥感图像进行了变化检测。实验证明,将特定目标的GIS辅助数据引入变化检测方法中能够大大提高检测精度和效率。     

基于高斯过程的高分辨率遥感图像变化检测

本文首先通过理论分析,探讨了高斯过程分类器在高分辨率遥感图像变化检测应用中的优势与不足,并针对高斯过程分类器的不足给出了相应的解决方法;其次,提出了一种基于空间上下文相关的高斯过程变化检测方法;最后,通过多个高分辨率遥感实验数据集上的实验设计与分析,验证了高斯过程分类器在高分辨率遥感图像变化检测中的应用能力,并证明了本文提出的解决方法的有效性.     

基于FastICA算法的遥感图像中地物变化检测

随着遥感技术的发展,地物变化检测在环境灾害评估、森林资源、土地资源、农业资源的动态变化检测等领域应用广泛,成为当前遥感图像应用的一个重要研究方向。采用能够消除信号间的高阶相关信息的快速独立分量分析法进行多时相多光谱遥感图像的变化检测,应用Open RS平台,实现基于Fast ICA的遥感图像变化检测算法,用检测概率、漏警率、虚警率、检测误差以及Kappa系数来进行多时相遥感图像的变化检测精度验证。     

加入多时相纹理的遥感变化检测

基于遥感技术的变化检测是遥感应用的一个重要方面.传统基于遥感的变化检测方法一般是利用光谱信息,较少注意多时相图像间的光谱特征相关关系分析.本文运用地统计学的伪交叉变差函数(Pseudo Cross Variogram)计算多时相图像纹理,定量表达多时相图像间的空间相关关系,并将得到的多时相纹理信息与光谱信息一起用于多时相变化检测.实验结果表明,加入多时相纹理信息可以显著提高变化检测精度,是一种有效的方法.     

遥感影像与GIS分析相结合的变化检测方法

提出了基于遥感图像和GIS相结合的变化检测和分析方法，并针对基于像素信息比较的遥感图像变化检测中的变化闽值问题提出了一种基于多边形面积填充率的自适应确定方法。     

面向对象的遥感变化检测研究

通过分析传统的遥感变化检测方法存在的问题,提出了面向对象的遥感变化检测方法。本文利用某地ETM+两个时相的遥感影像,将面向对象和传统变化检测方法进行定性定量的比较,从而得出面向对象的遥感变化检测方法的优势。该方法采用了基于相邻影像区域合并异质性最小的面向对象的多尺度分割方法和模糊分类的方法对变化检测图像进行处理,从而提高了变化检测结果的精度。最终得到较理想的实验分析结果。     

A New Approach to Improve the Cluster-based Parallel Processing Efficiency of High-Resolution Remotely Sensed Image

Object-oriented remotely sensed images processing method has been accepted by more and more experts of remote sensing. To advance the efficiency of data processing, parallel image computing is a good choice since large volumes of data need be analyzed efficiently and rapidly. This paper presents the information extraction method based on per-parcel extraction of high-resolution remotely sensed image; to extract efficiently different information from remotely sensed image, this paper gives the research idea of image rough-classification based on large-scale and subtle-segmentation based on small-scale; to improve the efficiency of image processing, we adapt parallel computing method to solve this problem by presenting an new data-partition method. At last this paper gives the implementation of the research idea based on Message Passing Interface (MPI) and analyzes our experimental system efficiency, and the results show that the new methods can improve the efficiency of high-resolution remotely sensed image data processing efficiently and have a good application.     

基于典型相关分析的多元变化检测

通过对传统变化检测方法在不足的分析,引进典型相关分析的基础理论,将不同时相的多通道遥感数据视为分组的多元随机变量,利用典型我进行遥感数据的多变换进行遥感数据的多元化检测。实验结果表明,所提出的Ｍ变换方法用于多时相、多通道遥感影像的变化检测具有明显的优势和应用前景。     

Change detection from remotely sensed images: From pixel-based to object-based approaches

The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.     

基于Web Services的遥感图像分布式处理

遥感影像获取手段的多样化导致了遥感影像数据量的大幅度增加,应用分布式环境处理遥感图像变得越来越迫切。本文针对B/S或C/S模式在遥感影像分布式处理方面的弊端,提出了在.Net环境下基于Web Serv-ices实现遥感图像的分布式处理方案,对其框架、流程进行了设计,并以图像处理的算法分布式为例详细介绍了边缘提取中拉普拉斯8邻域提取的具体Web Services实现。结果表明,新模型下的系统在多用户并发访问等方面都有很大的改善。     

龙羊峡库区环境动态监测信息系统的建立与应用

以黄河上游最大的水利工程－－龙羊峡水库为实验区,围绕库区存在的主要环境问题的研究,应用遥感与ＧＩＳ一体化的方法,研究设计了系统结构、功能及数据库的组成,并建立了可运行的龙羊峡库区环境动态监测信息系统,在系统支持下有效的实现了库区环境动态监测与应用,由于该系统较强的可操作性和适应性,其成果在高寒干旱与半干旱地区环境研究中具有推广和应用价值。     

基于空间数据挖掘的遥感图像处理

数据与数据库的爆炸式增长导致了一个十分突出的问题,即如何高效、智能地从巨量的、有噪音的、随机的数据中提取有效的、潜在有用的信息和知识.近几年来,空间数据挖掘技术的广泛研究正是基于此目的.本文初步探讨了空间数据挖掘技术在遥感图像处理中的应用,其重点阐述了关联规则,以及数据挖掘技术在遥感图像数据处理中的基本方法以及如何对遥感图像数据进行离散化处理.文章最后简要介绍了遥感图像处理的决策树和人工神经网络数据挖掘技术方法.     

遥感信息处理不确定性的可视化表达

如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验.     

Impact of discretization methods on the rough set-based classification of remotely sensed images

Abstract

In recent years, the rough set (RS) method has been in common use for remote-sensing classification, which provides one of the techniques of information extraction for Digital Earth. The discretization of remotely sensed data is an important data preprocessing approach in classical RS-based remote-sensing classification. Appropriate discretization methods can improve the adaptability of the classification rules and increase the accuracy of the remote-sensing classification. To assess the performance of discretization methods this article adopts three indicators, which are the compression capability indicator (CCI), consistency indicator (CI), and number of the cut points (NCP). An appropriate discretization method for the RS-based classification of a given remotely sensed image can be found by comparing the values of the three indicators and the classification accuracies of the discretized remotely sensed images obtained with the different discretization methods. To investigate the effectiveness of our method, this article applies three discretization methods of the Entropy/MDL, Naive, and SemiNaive to a TM image and three indicators for these discretization methods are then calculated. After comparing the three indicators and the classification accuracies of the discretized remotely sensed images, it has been found that the SemiNaive method significantly reduces large quantities of data and also keeps satisfactory classification accuracy.     

Remote sensing research issues of the National Land Use Change Program of China

In 1999, the Ministry of Land and Resources (MLR) of China launched the National Land Use Change Program especially to monitor the scale and distribution of urban expansion and the decrease in cultivated land through remote sensing technology. This Program has been carried out annually and continuously for seven years since then and played an important role in the policy-making of MLR about land management and planning. This paper gives an overview about this Program and discusses several research issues. First, the remote sensing data sources and other ancillary data used in this Program are presented. The approaches for image preprocessing, i.e. radiometric normalization, image geometric rectification and image fusion are then introduced with an emphasis on the algorithm development for image registration. Second, land use change detection technique is the most critical and complex aspect of the Program. The methodologies for change detection using either bi-temporal image pair or one existing land use map and one remotely sensed image are detailed. Third, since the data of land use changes derived from remote sensing will be operationally used for local and central government, field validation and accuracy assessment are crucial to ensure the reliability of change detection results. The strategy of field work and the resulting accuracy evaluations is presented. The land use and change information derived from remotely sensed data has wide applications for land management, including land use database updating, verification of land use planning and monitoring of national high-tech parks. Last, suggestions on how to make full use of the images and change detection result, to improve the consistency of land use classification and to develop change detection algorithms for diverse and complex remote sensing data are given.     

基于形态分割的高分辨率遥感影像道路提取

基于灰度形态学,提出一种从高分辨率遥感图像提取道路网络的方法.首先利用灰度形态特征对遥感影像进行分割,进而得到基本的道路网络轮廓.然后在此基础上,利用线段特征匹配方法提取道路网络.提出的方法能适应于从道路和背景区别不很清楚的遥感图像中提取道路.实验结果也表明,本文方法能有效地从遥感影像中提取道路网络.