【作者】 张路；

【导师】 廖明生；

【作者基本信息】 武汉大学 ， 摄影测量与遥感， 2004， 博士

【摘要】 基于遥感影像的变化检测就是从不同时间获取的遥感影像中,定量地分析和确定地表变化的特征和过程的技术。利用不同时相获取的卫星遥感影像进行变化检测,是开展资源调查、环境监测、基础地理数据库更新等对地观测技术应用中的关键技术,具有迫切的科学应用需求和广泛的应用领域。例如,如何从遥感影像中提取地表覆盖变化信息,近年来已成为遥感应用领域的重要研究课题,其中基于多时相、多通道(多波段或多极化等)的遥感影像的变化检测更是一大研究热点。本论文主要围绕着如何从多时相的中分辨率星载多光谱遥感影像以及合成孔径雷达影像中快速有效地自动提取变化信息来展开研究,重点解决不同时相之间的差异影像的构造和变化区域的提取这两方面的关键技术。 论文首先研究了从多时相多通道遥感影像构造差异影像的问题。多通道遥感影像由于通道之间相关性的影响,相对于单通道影像的变化检测更为困难,需要有效地集中分布在各个通道上的变化信息,构造出不同时相之间的差异影像,以便于变化信息的分析解译。本文针对多通道变化信息集中的难点,从多元统计分析的角度出发,在以下三个方面进行了深入研究: (1)针对通道之间相关性的影响难以消除的问题,引入多元统计分析中的典型相关分析方法,将两个时相的多通道遥感影像视作两组多元随机变量,采用Multivariate Alteration Detection(MAD)变换,对多个波谱通道上的所有差异信息或变化信息进行重组,分配到一组互不相关的结果变量中,最大限度地消除通道间相关性对变化检测的不利影响,初步解决了差异影像构造的问题。 (2)针对MAD方法难以完全有效地集中变化信息的问题,提出以信噪比作为衡量变化信息分布的测度,引入最小噪声比率变换MNF,实现MAD结果中包含的变化信息与噪声最大限度的分离,解决了有效集中变化信息和构造差异影像的问题。 (3)探讨了利用来自不同传感器的遥感影像,进行直接比较像元灰度特征的变化检测的可行性。提出了采用MNF/MAD方法来从多源多通道遥感影像构造差异影像的方案,在Landsat7 ETM+与SPOT5 HRG影像上进行的变化检测实验结果证实了该方案的有效性。 多个试验区的实验结果表明,基于典型相关分析的MNF/MAD多元变化检测方法,能够有效地从多时相的多通道遥感影像中分离出变化信息,并集中到差异影像的少数分量中,这些分量通常能够表现出较为明晰的物理意义。同时,这种方法具有对量测尺度不一致、量测设备增益变化、线性辐射畸变等不敏感的优点,因而对辐射特性归一化的要求很低,降低了数据预处理的难度。 然后,论文研究了从变化检测得到的差异影像中提取变化区域的问题。变化更多还原

【Abstract】 Change detection in remotely sensed imagery is defined as the procedure of quantitatively analyzing and identifying changes occurred on the earth’s surface from remotely sensed imageries acquired at different times. As a key element for many applications of earth observation such as resource inventory, environment monitoring, update of fundamental geographical database, etc., change detection technique is of urgent demands and has great potential in scientific applications. Currently change detection, especially change detection based on multi-temporal multi-channel (multispectral, multi-polarization, etc.) remotely sensed imageries has become a hot topic in research field related to remote sensing applications.Significant efforts have been made in the development of change detection techniques, and quite a lot of methods have been devised. However, there are still some problems that could not be solved properly by traditional methods in change detection, such as concentration of change information on all channels to produce temporal difference images, extraction of changed areas, identification of change types, etc. Under such circumstances, our investigations are carried out around the issues related to how to automatically extract change information rapidly and effectively from multi-temporal spaceborne remotely sensed multispectral imageries with mid-resolution, as well as Synthetic Aperture Radar (SAR) imageries in this dissertation. Most efforts are focused on two key problems, including production of temporal difference images and extraction of changed areas.The problem of producing difference images from multi-temporal multi-channel remotely sensed imageries is investigated in the first part of this dissertation. Compared with change detection based on single-channel imageries, it is more difficult to perform change detection on multi-channel imageries due to impact of inter-channel correlations. And it is necessary to effectively concentrate change information from all channels to produce a temporal difference image to facilitate detection and analysis of changes. From the point of view of multivariate statistical analysis, thorough researches are conducted in following aspects:(1) To eliminate impact of inter-channel correlations, canonical correlation analysis (CCA) and the so-called multivariate alteration detection (MAD) method based on CCA are introduced into bi-temporal multi-channel change detection. According to MAD method, two multi-channel imageries covering the same geographic location and acquired at different times are taken as two sets of random variables, then MAD transformation is performed on these random variable sets to produce a set of result variates that are uncorrelated with each other. In this way correlations between channels can theoretically be removed as much as possible, so that the actual changes in all channels can be simultaneously detected in the resultant difference image.(2) To improve effectiveness of the MAD result, it is proposed to use signal-to-noise ratio (SNR) instead of variance as a measurement for change information distribution, and another multivariate statistical transformation called minimum noise fraction (MNF) is introduced as a post-processing step for MAD transformation. In this way, change information can be separated from noise to the greatest extent, so that the technical problem of effectively concentrating change information and producing difference image could be solved properly.(3) The feasibility of change detection based on direct comparison usng multi-temporal remotely sensed imageries acquired by multi-sensors is explored. The scheme of employing MNF/MAD to produce difference image is proposed for multi-sensor change detection. An experiment on Landsat7 ETM+ and SPOT5 HRG imageries is carried out to demonstrate the effectiveness of the proposed scheme.Experimental results in a few test sites indicate that MNF/MAD method based on CCA is able to extract change information effectively from multi-temporal multi-channel remotely sensed imageries and pool them into a few resultant components of the temporal difference image. Generally these components could manifest some clear physical meanings. A distinguished advantage of the MNF/MAD scheme is its invariant to linear scaling, which means it is insensitive to disagreement in measurement scale, gain settings in measuring devices, and linear radiometric distortions, as a result the requirement on image preprocessing could be reduced.In the second part of this dissertation, the problem of extracting changed areas from difference image produced by change detection is studied. In fact changed area extraction is a typical problem of two-category classification, and can be solved by employing thresholding strategy. However, thresholds are difficult to establish in traditional schemes. In virtue of theories and methods in statistical pattern recognition, thorough researches are conducted in following aspects:(1) A method based on Bayes Rule for Minimum Error is proposed to establish change thresholds in an automatic way. Upon analyzing statistical characteristics of difference image, we firstly assumed that both the pixels of change and that of no change were subject to simple Gaussian density distribution model, and employed the Expectation-Maximization algorithm to estimate distribution parameters and change thresholds, so as to extract changed areas in an automatic way. Then, to account for the difficulty of applying simple Gaussian density distribution model in describing complicated distributions containing multiple classes, the mixed Gaussian density distribution model is used instead to describe distributions of the two pixel classes. And accordingly genetic algorithm is employed to estimate distribution parameters, so as to improve estimation of change thresholds.(2) A deficiency of Bayes scheme is found to be the adoption of pixel independency assumption as well as ignoring contextual information. Contextual Bayes decision method is devised for this problem. In this method, Markov random field (MRF) model is introduced into Bayes decision to depict and utilize contextual information to estimate local prior probability, so as to improve accuracy and reliability of the changed area extraction results.(3) The problem of SAR change detection is studied. The scheme of ratioinp with logarithmic stretching is employed to produce a temporal difference image. According to the approximate Gaussian distribution characteristics of pixels in difference image, a scheme is proposed to apply the contextual Bayes decision method to extract changed areas from the difference image.Experimental results demonstrate that for both multi-temporal optical and SAR imageries acquired by spaceborne sensors, the contextual Bayes decision method could establish change threshold in an automatic and unsupervised way, thus could identify and extract change areas effectively from the difference image.更多还原