【作者】 汤玉奇；

【导师】 张良培； 李平湘；

【作者基本信息】 武汉大学 ， 摄影测量与遥感， 2013， 博士

【摘要】 遥感影像空间分辨率的提高为城市发展的监测与规划提供了大量地面细节信息,使得城市遥感变化检测研究成为当前遥感研究领域的热点之一。然而,数据信息量的急剧增加,也为遥感影像变化检测技术的发展提出了新的问题与挑战：首先,丰富的地物细节信息使得单一地物对象由多个空间相邻的像元组成,单个像元的光谱变化不足以反映其所在地物的变化情况；其次,影像空间分辨率提高、光谱分辨率受限,导致同类地物光谱差异变大、不同地物光谱相互重叠,“同物异谱、异物同谱”的现象普遍存在；再次,多时相高分辨率影像成像条件的差异,导致同一地物在不同时相的影像中呈现出光谱、空间特征的差异,仅仅通过影像预处理很难彻底消除这些差异,例如：针对海拔高度较高的地物,多时相成像角度的差异将严重影响变化检测效果；最后,成倍增长的多时相影像数据量使得对算法自动性的要求更高。本文在现有变化检测技术的基础上,针对高分辨率遥感影像中的地物对象变化情况,提出了几种新的面向对象的多特征变化检测模型。它们分别着眼于改善面向对象的变化检测方法中的对象“匀质性”问题,提高自动搜索全局最优的变化检测结果的能力,解决多源影像光谱分辨率差异、多时相影像复合分割误差的问题,改进多时相影像房屋变化显著性度量方式,以及提高针对多时相成像角度差异带来的房屋“伪变化”的容错性变化检测能力等等。通过利用QuickBird、 IKONOS等高分辨率遥感卫星影像数据进行实验,验证了各种检测模型的有效性。为了引入本文提出的变化检测模型,我们首先总结了传统遥感影像变化检测方法的基本思路,详细介绍了影像预处理、变化信息提取、阈值选择与精度评价这四项关键技术。其中,在变化信息提取的部分,详细介绍了现有的基于像元光谱信息的变化分析方法中的代数运算法、影像变换法,以及顾及影像空间信息的面向对象的方法和基于神经网络的方法,并运用一组共用多时相QuickBird影像数据对这些方法进行了实验验证与分析。结果证明：传统的基于像元光谱信息的变化检测算法由于没有考虑影像空间上下文信息,已无法满足高分辨率影像变化分析的需要；而现有的顾及影像空间信息的变化检测算法虽然实现了对高分辨率影像空间信息的利用,但仍存在一些问题,包括面向对象的方法中的对象“匀质性”问题、复合影像分割失真的问题以及多时相影像成像角度差异对变化检测结果的影响间题等等。由此引出了本文针对这些问题的具体解决方案：首先,针对高分辨率遥感影像面向对象的变化分析的两大关键问题——阈值选择对自动获取全局最优解的影响以及面向对象方法的对象“均值化”问题,本文提出了两种新的检测模型,在面向对象的思想下,分别利用遗传算法(Genetic Algorithm,GA)自动搜索全局最优解的机制,以及针对多时相影像对象内部像元光谱特征的K-S (Kolmogorov-Smirnov)统计检验,有效解决了上述问题。根据两组多时相QuickBird影像数据的实验证明,基于GA的方法能够通过循环迭代中的遗传操作自动搜索全局最优的地物对象变化检测结果,避免了阈值选择对算法自动性和最优解选择效果的影响：而基于K-S检验的方法有效保留并考察了多时相影像对象内部的像元光谱统计差异,解决了传统面向对象的方法中的对象“均值化”问题,从不同角度提高了高分辨率遥感影像面向对象的变化检测的有效性。其次,通过分析总结多源高分辨率影像变化分析与同源影像的区别与联系,总结了当前多源影像变化分析的难点,即多源影像的光谱分辨率差异问题。针对这一问题,我们提出了一种新的解决方案,根据变化区域与其所在对象的空间关系,定义多时相影像对象的相似性特征,提取相似性较小的影像对象并将其视为变化的影像区域。同时,该方法通过多时相影像分割映射的方式解决了影像复合分割误差的问题,并针对不同基准影像与不同空间尺度下的多类变化检测结果进行了影像区域融合的后处理。根据两组获取自QuickBird与IKONOS卫星传感器的多源多时相影像数据的实验验证与分析,证明了该方法能有效检测多源高分辨率影像的变化区域。最后,为了针对性地监测反应城市发展的房屋目标变化情况,我们总结了高分辨率影像房屋变化分析现存的两大问题：变化显著性度量方式与多时相成像角度差异的影响。首先,针对变化显著性度量方式的问题,提出了基于脉冲耦合神经网络(Pulse-Couplec Neural Network, PCNN)的房屋变化检测方法,通过神经网络的构建,充分考虑各时相房屋特征影像的对象空间上下文信息,并使用多种相关性度量方式,全面考察房屋对象的变化显著性程度,并据此判断房屋对象的变化情况。通过两组多时相QuickBird影像的实验验证,证明了该方法能有效提取高分辨率影像的房屋变化区域。其次,为了尽可能地降低多时相成像角度差异对房屋变化检测结果的影响,房屋容错性变化检测方法通过对多时相房屋特征点的局部影像匹配,容错性地识别出不同时相中空间几何分布特征存在差异的同一房屋对象,并将其从真实变化的房屋区域中剔除。通过多组QuickBird或IKONOS影像实验证明,该方法能有效降低多时相成像角度的差异导致的对房屋“伪变化”的误检,明显提高了房屋变化检测的精度。更多还原

【Abstract】 The mass information consisted in the remote sensing images with increasing spatial resolution make it possible to detect changes with a finer scale in urban areas. It makes the change detection to be a hot topic in researches on the high-resolution remote sensing images, whereas taking challenges to the existing change detection techniques as follows:1) the spectral variation of a single pixel can not reflect the transformation of the observed object covering it since the object is consisted of a cluster of pixels spatially connected;2) the increasing spatial resolution and limited spectral resolution lead to the increasing spectral deviation inter-classes and spectral overlaping intra-classes in the high-resolution images, which restrains the partibility of image spectrum;3) the diversity on receiving status of mutlitemporal images results in the spatial shifting and stretching of observed objects, especially the ones with elevation; and4) the increasing data amount claims higher automaticity of techniques.In order to detect the changes in the high-resolution remote sensing images, several change detection models are proposed in this paper. They are respectively devoted on ameliorating the problem of "smoothed object" in the object-oriented methods, improving the ability of automatically and globally searching the optimal solution, resolving the diversity of spectral resolution between multi-source images and the errors from compound segmentation of multitemporal images, measuring the similarity of multitemporal images by novel means, and enhancing the fault-tolerance to the "building pseudo changes" resulting from different receiving angles of multitemporal images. The effectiveness of these models was confirmed in the experiments with real high-resolution remote sensing images.As the background of our works, we introduce the four crucial procedures in the change analysis of remote sensing images, including the image preprocessing, change extraction, threshold selection and accuracy assessment. In the reviewing of change information extraction, the existing methods are categorized into two kinds, the methods considering only the pixel spectrum or together with the spatial information. The methods only considering the pixels spectrum includes the methods based on the algebraic operations and image transformations. And the methods combining the spectral and spatial information are based on the object-oriented theary and neural networks. The experiments with a dataset of multitemporal QuickBird images proved that the methods considering only the pixel spectrum could not meet the need of high-resolution images as neglecting the spatial context information, whereas the methods considering spectrum together with the space could solve it. However, the existing techniques considering the spectrum and space are comfronted with some problems, such as the "smoothed object" and the segmentation errors of compound image.First of all, two innovative models are proposed to respectively solve the two critical problems of the object-oriented change detection in the remote sensing images. The searching of the optimal resolution resolved by taking using of the mechanism of automatically and globally searching the optimal solution in the genetic algorithm (GA), and the K-S test is employed to measure the statistic characteristic of multitemporal objects to solve the "smoothed object" problem. The experiments with two sets of QuickBird images proved that the method based on GA could avoid the effect of threshold selection, and the method based on the K-S test could effectively reserve the spectral variance in the image objects. Both of the methods improve the object-oriented change detection.Secondly, comparing the change detection in multi-source images to the one in single-source images, we introduce the difficulty of the former, namely the different spectral resolutions between the multi-source images. Our solution is defining a similarity measure of the multitemporal objects based on the spatial relations between the changed areas and image objects, and judging the objects with lower similarity as the changed areas. Meanwhile, the errors of compound segmentation is overcome by segmentation mapping between the multitemporal images. The experiments with two sets of multi-source images, which was respectively acquired by the QuickBird and IKONOS satellites, proved the effect of this method to detect changes in the multi-source remote sensing images.In our works on the building change detection, we conclude the two main problems:the change dominance measurement and the effect of different multitemporal viewing angles. In order to define a new change dominance measure, we propose to use the pulse-coupled neural network (PCNN) to detect building changes, and exploit several correlative measures to investigate the change probability of each building object. The experiments with two sets of QuickBird datasets proved the effectiveness of this method. On the other hand, we define a fault-tolerant building change detection method to recognize and delete the "pseudo" changing buildings by local registration of the multitemporal building feature points. This method was proved to be effective to suppress the commissions from different multitemporal viewing anlgles, and improve the accuracy of building change detection.更多还原