一种多特征融合的面向对象多源遥感影像变化检测方法

当前,随着遥感影像数据来源越来越丰富,且分辨率越来越高,传统的变化检测方法已经无法满足实际应用的需要。针对这一问题,提出了一种多特征融合的面向对象多源遥感影像变化检测方法。在对象获取和多种特征提取的基础上,利用SVM对高维数据分类的优异特性,将基于SVM的二类分类方法与对象级变化检测有机结合,提高了多源遥感影像变化检测的精度和可靠性。结合人工目视判读,设计了一种面向地物的指标计算方法。实验采用多源多时相的遥感影像进行,并对不同地物变化检测的精度进行统计,验证了提出方法的有效性。     

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。     

联合像素级和对象级分析的遥感影像变化检测

为改善高空间分辨率遥感影像的变化检测精度,提出一种联合像素级和对象级分析的变化检测新框架。首先将多时相影像进行叠合,对叠加影像进行主成分分析,并利用基于熵率的方法对第一主成分影像进行分割,通过改变超像素数目来获取多层次不同尺寸大小的超像素区域。同时,对多时相影像进行光谱差异和纹理差异分析,采用自适应PCNN神经网络方法进行图像融合,利用水平集(CV)方法对融合后的影像进行分割获取像素级变化检测结果。最后,结合多尺度区域标记矩阵对检测结果进行变化强度等级量化和决策级融合,作为变化检测的后处理部分,以获取最终的对象级变化检测结果。采用SPOT-5多光谱影像进行试验。结果表明这种新框架可以有效集成基于像素和基于对象两种图像分析方法的优势,能够进一步提高变化检测过程的稳定性和适用性。     

ALOS数据像素级融合方法比较研究

遥感数据融合是多源遥感海量数据富集表示的有效途径。如何在提高融合影像空间分辨率的同时最大限度地保持光谱信息是长期以来遥感数据融合研究的焦点内容。本文以ALOS PRISM和ALOS AVNIR-2传感器的数据为数据源,比较研究了遥感领域中常用和代表性的BROVEY、IHS、MULTIPLICATIVE、PCA、WAVELET和HPF六种融合方法,并通过主观评价和定量分析对融合效果进行了综合评价。实验结果表明,HPF方法在显著提高融合影像空间分辨率的同时,有效保持了多光谱影像的光谱信息,是适合ALOS数据的最优融合方法。     

孪生高分辨率卷积神经网络遥感影像变化检测方法

针对遥感影像变化检测问题,提出了一种孪生高分辨率卷积神经网络模型。该模型首先基于孪生网络模型提取不同时相遥感影像的特征,然后将特征拼接后输入到嵌套U形网络中输出变化检测区域。为了提升变化检测效果,进一步设计了高分辨率卷积神经网络用于提取不同时相遥感影像的特征,以充分利用不同分辨率的特征来提升变化检测效果。在LEVIR-CD变化检测数据集上的大量实验表明,所提出方法能够比对比方法获得更高的变化检测精度。     

联合显著性和多方法差异影像融合的遥感影像变化检测

针对高空间分辨率遥感影像地物复杂、传统变化检测方法漏检率高的问题,提出了一种联合显著性和多方法差异影像融合的多时相遥感影像变化检测方法。选取3组双时相高空间分辨率遥感影像作为实验数据,首先分别采用变化矢量分析(change vector analysis,CVA)和光谱斜率差异(spectral gradient difference,SGD)两种方法对两个时相遥感影像进行对应的差异影像构造;然后通过基于聚类的联合显著性方法分别获取两幅差异影像的联合显著性图;最后,将两幅联合显著性图进行融合得到联合显著性差异图,并采用大津法(OTSU)对联合显著性差异图进行阈值分割和闭运算得到最终变化图。实验表明,该方法的总体精度(overall accuracy,OA)、Kappa系数和F-measure精度优于传统方法,可靠性强,具有很高的准确性。     

结合变化向量分析和直觉模糊聚类的遥感影像变化检测方法

针对多时相遥感影像变化检测存在数据不确定性、检测精度不高等问题,提出了一种结合变化向量分析（CVA）和直觉模糊C均值聚类算法（IFCM）的多时相遥感影像变化检测方法. 首先通过CVA构建两个时相遥感影像的差异影像;然后采用直觉模糊C均值聚类算法对差异影像进行聚类得出变化区域和未变化区域;最后对变化检测结果进行二值化处理并进行精度评价. 选取两个时相的高分一号遥感影像和Szada数据集影像作为实验数据. 实验结果表明,采用提出的方法可有效解决传统方法存在的数据不确定性问题,变化检测精度达到了95.92%和92.70%,是一种可行的遥感影像变化检测方法. 研究结果可用于森林动态变化监测、土地复垦利用规划变化分析以及灾损评估.      

多时相遥感影像变化检测综述

近几十年来,多时相影像处理和变化检测是遥感领域一个较为活跃的研究方向。虽然在环境变化监测和检测方面成功的应用有很多,但是从多时相影像及时提取地球环境和人类活动的动态信息仍匝临很多挑战。近年来,随着新型遥感平台和传感器的发展,在克服技术障碍方面取得了很大进步。历史影像档案提供范围的扩大也使得长期变化检测和建模成为可能。这些发展推动了更先进的影像处理方法的开发和处理时间维度影像数据新方法的研究工作。本文回顾了多时相数据处理和变化检测的发展现状。作者首先对地球表面环境变化的性质进行了分析,从四个方面总结了遥感变化检测技术的发展,包括变化检测影像预处理、变化检测方法和途径分类、遥感变化检测方法和多时相影像处理的准确度评估。文章试图在先验知识的基础上从多源数据、集成处理和智能方法角度提出综合解决方案。为促进遥感影像变化检测技术的深入研究,本文还指出了变化检测所面临的挑战和可能的对策。     

利用最小交叉熵的多时相影像变化检测

针对目前多时相遥感影像变化检测中缺少有效的确定变化阈值方法的问题,文章提出一种基于最小交叉熵的方法:采用中值滤波方法分别消除两个时相遥感影像中的噪声;将比值法和差值法融合构造两个时相遥感影像的差异影像;通过最小交叉熵确定差异影像的最佳变化阈值,并对差异影像进行分割得到变化区域;最后对变化检测精度进行评估。实验结果表明,提出的方法具有较高的检测精度,是一种有效的多时相遥感影像变化检测方法。     

融合多特征的遥感影像变化检测

常规多时相遥感影像变化检测主要基于光谱信息,没有充分利用纹理、几何、形状等多种特征信息,不足以体现检测目标的完整性和准确性。本文针对不同特征在变化检测中应用的优势,在提取影像多种特征的基础上,构建了1维和多维两种基于信息融合策略的变化检测方法,即利用1维特征空间加权距离相似度运算、多维特征空间的模糊集融合和支持向量机融合策略进行变化检测。利用多时相QuickBird高分辨率遥感影像进行城市土地覆盖变化检测试验,结果表明,本文方法可以有效集成不同特征的优势与表征变化信息的能力,提高变化检测过程的稳定性和适用性,同时能够更好地保持变化地物的结构和形状,突出主要变化目标。     

Applications of remote sensing and geographic information systems for urban land-cover change studies in Mongolia

The aim of this study is to compare the changes that occurred in the main urban land-cover classes of Ulaanbaatar city, Mongolia, during a centralized economy with those that occurred during a market economy and to describe the socio-economic reasons for the changes. For this purpose, multi-temporal remote sensing and geographical information system (GIS) data sets, as well as census data, are used. To extract the reliable urban land-cover information from the selected remotely sensed data sets, a refined parametric classification algorithm that uses spatial thresholds defined from local and contextual knowledge is constructed. Before applying the classification decision rule, some image fusion techniques are applied to the selected remotely sensed data sets to define the most efficient fusion method for training sample selection and for defining local and contextual knowledge. Overall, the study indicates that during the centralized economy significant changes occurred in a ger area of the city, whereas during the market economy the changes occurred in all areas.     

Markovian Fusion Approach to Robust Unsupervised Change Detection in Remotely Sensed Imagery

The most common methodology to carry out an automatic unsupervised change detection in remotely sensed imagery is to find the best global threshold in the histogram of the so-called difference image. The unsupervised nature of the change detection process, however, makes it nontrivial to find the most appropriate thresholding algorithm for a given difference image, because the best global threshold depends on its statistical peculiarities, which are often unknown. In this letter, a solution to this issue based on the fusion of an ensemble of different thresholding algorithms through a Markov random field framework is proposed. Experiments conducted on a set of five real remote sensing images acquired by different sensors and referring to different kinds of changes show the high robustness of the proposed unsupervised change detection approach     

基于边缘特征匹配的遥感影像变化检测预处理方法

提出了一种基于边缘特征匹配的遥感影像变化检测预处理方法，在进行不同时相遥感影像配准时，一并解求两期影像的辐射校正系数，同时实现两期影像的配准和辐射校正。实验表明，经预处理后，两期影像的色调基本一致，地物没有明显的几何变形。     

Change detection assessment using fuzzy sets and remotely sensed data: an application of topographic map revision

This paper explores a methodology for computing the amount of changes that have occurred within an area by using remotely sensed technologies and fuzzy modelling. The discussion concentrates on the formulation of a standard procedure that, using the concept of fuzzy sets and fuzzy logic, can define the likelihood of changes detected from remotely sensed data. Furthermore, an example of how fuzzy visualisation of areas undergoing changes can be incorporated into a decision support system for prioritisation of areas requiring topographic map revision and updating is presented. By adapting the membership function of the fuzzy model to fit the shape of the histogram characterising the change image (derived from any of the common pre-classification methods of change detection), areas can be identified according to their likelihood of having undergone change during the period of observation.     

基于IHS变换和小波变换的遥感影像融合

在遥感影像融合中，IHS变换法与小波变换法具有互补性，文中把这两种方法结合起来，提出了一种基于IHS变换与小波变换的影像融合方法。通过对具体影像的实验证明，该方法是有效的，达到了预期的目的。     

Detecting land-use/land-cover change in rural–urban fringe areas using extended change-vector analysis

Detecting land-use/land-cover (LULC) changes in rural–urban fringe areas (RUFAs) timely and accurately using satellite imagery is essential for land-use planning and management in China. Although traditional spectral-based change-vector analysis (CVA) can effectively detect LULC change in many cases, it encounters difficulties in RUFAs because of deficiencies in the spectral information of satellite images. To detect LULC changes in RUFAs effectively, this paper proposes an extended CVA approach that incorporates textural change information into the traditional spectral-based CVA. The extended CVA was applied to three different pilot RUFAs in China with different remotely sensed data, including Landsat Thematic Mapper (TM), China–Brazil Earth Resources Satellite (CBERS) and Advanced Land Observing Satellite (ALOS) images. The results demonstrated the improvement of the extended CVA compared to the traditional spectral-based CVA with the overall accuracy increased between 4.66% and 8.00% and the kappa coefficient increased between 0.10 and 0.15, respectively. The advantage of the extended CVA lies in its integration of both spectral and textural change information to detect LULC changes, allowing for effective discrimination of LULC changes that are spectrally similar but texturally different in RUFAs. The extended CVA has great potential to be widely used for LULC-change detection in RUFAs, which are often heterogeneous and fragmental in nature, with rich textural information.     

多源遥感影像像素级融合分类与决策级分类融合法的研究

首先探讨了基于像素的多源遥感影像高频调制融合法，根据成像系统特性和Heisenberg测不准原理，设计的高斯滤波器对高分辨率影像滤波的方法是合理有效的。在研究BP神经网络的基础上，采用动量法和学习率自适应调整的策略，提高了BP神经网络学习算法收敛速度，并增强了算法的可靠性。提出并实现了多源遥感影像像素级融合分类与决策级分类融合两种分类方法，并进行了比较。采用Landsat TM3，4，5和航空SAR影像进行试验，结果表明两种分类方法是行之有效的，均适用于多源遥感影像分类。     

基于多时相SAR数据的大区域城镇信息变化检测

多时相SAR变化检测技术,通过分析同一区域不同时相的SAR数据可以检测地表地物目标的变化信息,在土地资源调查及监测管理方面具有广泛应用。本文将长时间SAR图像相干性特征与目标幅度信息进行融合,并采用多时相SAR数据堆栈处理方法进行大区域城建目标分类及变化信息检测。最后采用13景ALOS PALSAR数据对中国天津地区2007~2010年期间的城建区域变化进行了检测实验,得到了良好的实验结果,并验证方法有效性。     

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.