基于PALSAR-1影像的汉函谷关遗迹变化检测研究

合成孔径雷达(SAR)具有全天候、全天时的特点,因此是很好的变化检测信息源。基于SAR的变化检测技术研究有着非常广阔的应用前景。以汉函谷关遗址为实验区,选取2007~2011年7景PALSAR-1影像为数据源,分别研究和比较了基于SAR强度信息和相干信息的变化检测技术,并对研究区拆迁前后地表变化进行了研究。结果表明:利用PALSAR-1相干信息和强度信息的变化检测在汉函谷关遗址动态监测中均具有良好适用性,检测概率分别为0.868和0.697,虚警概率分别为0.385和0.197。中分辨率PALSAR-1相干信息比强度信息对地表变化更为敏感且具有高的信噪比;而基于强度信息的变化检测方法因受斑噪与分辨率的限制,监测应用仍存在局限性。     

SAR变化检测技术发展综述

合成孔径雷达(SAR)具有全天候、全天时的特点,是很好的变化检测信息源,研究SAR图像变化检测技术有着非常广阔的应用前景。系统介绍了SAR变化检测的整个流程--数据预处理、变化检测算法、检测后处理及变化检测算法评价。为了方便算法设计者,分门别类地阐述了变化检测算法的主要方法及核心决策规则,包括差值法、统计假设检验法、预测模型、相干模型;并对每种方法的特征进行了分析和比较。针对SAR技术的迅猛发展,我们对将来SAR变化检测技术的发展方向给予了预测。     

SAR图像变化检测传统方法综述

合成孔径雷达（SAR）卫星具有全天候，全时间对地面情况监测的特点，在获得同一地点多时图像方面有着巨大优势。变化检测（change detection）是遥感领域中一个重要的应用手段，在洪水监测，建筑物变化监测，农作物生长情况，地震灾害灾情损失估计等方面有着广泛的应用。如今，合成孔径雷达中的变化检测方法逐渐变成未来遥感图像研究中的热点。在本文中，对几种合成孔径雷达的传统方法进行比较和分析。     

遥感影像变化检测算法综述

目的 近年来遥感技术发展迅速,遥感影像变化检测作为最重要的关键技术之一,越来越多的应用在国土资源管理、地物变化、农林业的监测等领域。目前依据变化检测的流程的综述相对较少,大多数综述只针对变化信息提取的方法进行论述,为使国内外研究者对遥感影像变化检测理论、流程及其现存问题有一个比较全面的认识,对其进行系统综述。方法 通过对国内外大量的遥感影像变化检测算法进行总结、分类及比较,按照技术流程的各个环节分别论述,重点论述了变化信息提取环节中的图像分割、特征提取和分类的发展现状、基本思想及其趋势。结果 目前,多数变化检测算法主要是针对特定的条件具有较好的效果,还没有通用性算法,且现有算法在效率、精度、智能性等方面存在的问题,大多算法解决的问题及理论相对分散。结合现存问题及目前大数据影响下的技术发展状况,从数据类型、预处理方法、变化信息提取方法、算法效率、算法理论创新5个方面对遥感影像变化检测领域的未来发展趋势进行预测和展望。结论 遥感影像变化检测在多领域具有较高的研究价值,但针对目前变化检测存在的一些局限性还需要进行深入的研究,针对变化检测的研究需要从研究热点中挖掘创新思路、引入深度学习等发展趋势。     

纹理特征在SAR图像变化检测中的应用

城区变化检测可以提供大量关于土地利用和城市发展的信息,是变化检测技术的重要应用之一。根据合成孔径雷达(SAR)图像特点,针对城区环境的特定应用,提出了纹理差值变化检测方法。该方法采用纹理特征代替灰度信息来体现图像特点,并使用差值变化检测来获取城区变化情况。实验结果表明,对于大多数特征,纹理差值法获得了较高的变化检测正确率,检测性能有了大幅度的提高。     

遥感影像变化检测综述

变化检测作为土地利用/土地覆盖检测的关键技术,其目的是在同一区域不同时期的遥感数据中检测出变化的部分及其类型。针对传统的变化检测方法中存在繁重的人工劳动和检测结果效果差等问题,大量基于遥感影像的变化检测方法被提出。为了深入了解基于遥感影像的变化检测技术以及进一步研究变化检测方法,通过对大量有关变化检测的研究进行整理、分析和比较,对变化检测进行了较为全面的综述。首先阐述了变化检测的发展历程;然后从数据选择及预处理、变化检测技术、后处理及精度评价这三个方面详细归纳了变化检测的研究进展,其中变化检测技术主要从分析单元和比较方法的方面分别进行概括;最后对变化检测各个阶段存在的问题进行了总结并提出了未来的发展方向。     

数据流变化检测研究综述

数据流是一种动态数据,它在某种因素的驱动下可能会随时间发生变化,而这种变化往往隐含着现实世界的某种事件。如何及时、准确地发现数据流中的变化已成为数据流挖掘的一个研究热点,并且在实际中有非常广泛的应用。描述了数据流变化及变化检测的核心任务,归纳了变化检测的通用框架,分析评价了目前已知的数据流变化检测方法及其技术特点,最后展望了数据流变化检测技术的发展方向。     

利用连续监视遥感图像进行的目标动态监测

利用同一地区不同时期变化检测技术，通过对获取的同一目标、不同时间遥感图像的分析，认为遥感技术对目标的动态监测具有重要的参考价值。     

基于边缘增强的遥感图像变化检测技术

遥感图像变化检测技术是遥感技术中最广泛最基本的应用之一。为此作者经过大量对已有图像的实验,提出一种基于边缘增强的遥感图像变化检测技术,通过选择适当的卷积算子对图像进行锐化增强与使用Sobel算子对图像进行边缘增强相结合,总结出了快速有效变化检测处理方法。     

基于D-S证据理论的遥感图像融合变化检测方法

基于像素级的遥感图像变化检测是利用配准后的不同时相遥感图像的原始像元灰度信息进行的变化检测,直观,易于理解,是目前应用较广泛的变化检测技术.由于现有算法无法在适应性、稳健性、准确性及时效性等方面实现性能全面占优,本文提出了一种基于D-S证据理论的融合变化检测方法,利用融合策略和规则在决策层对来自多种算法的检测结果进行综...     

Change Detection based on Difference Image and Energy Moments in Remote Sensing Image Monitoring

Permanent control of environment by using remote sensing images requires effective techniques. Two new methods for remote sensing image change detection are proposed. The first method is based on the notion of difference image and image histograms. A complementary pair of images is proposed as the main presentation of a difference image which allows automatic separation of the changes of ground objects without loss or distortion. The use of the histograms in accordance with variations of image brightness (increasing and decreasing) provides opportunities for the assessment and experimental verification of existing approaches in the selection of automatic detection thresholds. The second method for change detection is based on energy moments for image rows and/or columns. It allows one to find image changes even in one pixel and differs from the existed methods by a more simple algorithm and possibility to extract even small changes. The proposed image representation can be considered as an integral feature of the whole image. The methods have been tested in real images. Comparing to start-of-the-art methods, our methods can detect changes in real-time with high accuracy when deployed on a standard computer.     

Image change detection from difference image through deterministic simulated annealing

This paper proposes an automatic method based on the deterministic simulated annealing (DSA) approach for solving the image change detection problem between two images where one of them is the reference image. Each pixel in the reference image is considered as a node with a state value in a network of nodes. This state determines the magnitude of the change. The DSA optimization approach tries to achieve the most network stable configuration based on the minimization of an energy function. The DSA scheme allows the mapping of interpixel contextual dependencies which has been used favorably in some existing image change detection strategies. The main contribution of the DSA is exactly its ability for avoiding local minima during the optimization process thanks to the annealing scheme. Local minima have been detected when using some optimization strategies, such as Hopfield neural networks, in images with large amount of changes, greater than the 20%. The DSA performs better than other optimization strategies for images with a large amount of changes and obtain similar results for images where the changes are small. Hence, the DSA approach appears to be a general method for image change detection independently of the amount of changes. Its performance is compared against some recent image change detection methods.

Evaluation of the differencing pixel-by-pixel change detection method in mapping irrigated areas in dry zones

Geographic information system (GIS) and change detection techniques were used in this study to evaluate the efficiency of an automatic change detection method in detecting and mapping the changes that took place after the implementation of newly irrigated areas in dry zones. Two satellite images were used: Landsat Thematic Mapper (TM) 1983 and Landsat Enhanced Thematic Mapper Plus (ETM+) 2004. The newly implemented irrigated areas were identified using the differencing pixel-by-pixel change detection method. All of these areas were detected over the period 1983–2004. The accuracy of the results obtained from the change detection method applied was determined by spectral and spatial assessment (image interpretation and field surveying). It was shown that all the newly implemented irrigated areas were detected. The assessment of the areas of the new implemented parcels obtained by the current change detection method showed an error of less than 8%. This result is reasonably good if we consider the speed of the process and its low cost for large zones.     

基于DSM和影像特征的城市变化检测

随着城市的快速发展,城市的变化也越来越明显。为了能更好地为城市规划人员、城市管理决策人员及时提供信息,有必要对城市的变化发展进行适时地监测。利用大比例尺的航空影像来对城市地区的人工建筑物的变化进行检测,根据城市地区人工建筑物的变化主要反映在高度上的变化这一特点,利用两个不同时期的立体像对,从而生成两个不同时期的数字表面模型(DSM),在此基础上来检测出城市地区人工建筑物的变化区域。由于DSM影像上不仅有人工建筑物的变化反映在高度变化上,而且也有其它如树木等的变化反映在高度变化上。由于检测的目的是了解城市地区内人工建筑物的变化情况,所以利用DSM提出待选变化区域,在待选变化的区域中利用了梯度方向直方图、灰度匹配、及直线特征匹配等方法来针对城市地区人工建筑物的变化进行进一步的分析。利用日本的两个不同时期的1∶1万的航空影像进行了实验,从实验结果来看,所提出的方法是可行的,同时该方法简单、直观且易于操作。     

Use of metatruth image concept to assess forest change detection accuracy at pixel level

Traditionally, the validation of a classified multispectral image only quantifies its correspondence to ground reference data containing thematic information generalized at the stand level, with stands represented as vector polygons. Little is known of the accuracy of such classifications at a scale below the stand. This study presents a methodology to assess classification accuracy at pixel level, i.e. sub-polygon, where the classification procedure is embedded in a change detection environment. A new type of reference data (Metatruth Image) was generated based on the integration of the outputs of various independent change detection procedures. The integration consisted of calculating for each pixel a probability distribution or pixel purity index for each change class by independent change detection procedures, defined by the number of times the pixel has been classified as a certain change class. First, the relationship between purity and accuracy was successfully validated. Next, the Metatruth Image was created based on ‘high purity pixels’. Performing traditional accuracy assessment on the outputs of individual change detection procedures using the Metatruth Image as reference dataset, demonstrated that former outputs identified change events accurately at pixel level. As a consequence, traditional accuracy assessment at polygon level underestimates the true accuracy at pixel level of the change detection procedure in a systematic way with differences in kappa coefficients of agreement around 20%.     

通风机械仪表盘在复杂背景环境中视觉故障检测与定位研究

通风机械仪表盘往往处于复杂的背景环境中,阴影或部分遮挡会在图像中引入不一致的颜色、亮度和纹理变化,使得故障区域与周围环境的对比度下降,导致人工方法难以正确定位故障区域。针对这些问题,设计一种通风机械仪表盘视觉故障检测与定位方法。首先,使用Kinect相机提取通风机械仪表图像,并进行直方图均衡化来调节图像的亮度和色调,增强故障轮廓与背景的局部对比度。然后,利用改进像素相关性分割算法分割图像特征,将图像中的仪表盘区域从复杂背景中提取出来。利用深度学习领域的深度卷积网络,对分割后的仪表盘图像进行故障轮廓检测。最后,计算定位目标(故障轮廓)的质心坐标,将质心位置作为目标点,映射到构建的投影成像空间坐标系中实现对仪表盘显示故障区域的高精度定位。实验结果显示:应用该方法后,故障区域与周围环境的对比度区分显著增强,具有较高的检测和定位精度。     

Detecting the change of customer behavior based on decision tree analysis

Abstract: Understanding and adapting to changes in customer behavior is an important aspect for survival in a continuously changing environment. This paper develops a methodology based on decision tree analysis to detect the change in classified customer segments automatically between two data sets collected over time. We first define three types of changes as the emerging pattern, the unexpected change and the added/perished rule. Then, similarity and difference measures are developed for rule matching to detect all types of change. Finally, the degree of change is developed to evaluate the amount of change. Our suggested methodology based on decision tree analysis in the change detection problem can be used in more structured situations in which the manager has a specific research question and it also detects the change of classification criteria in a dynamically changing environment. A Korean Internet shopping mall case is evaluated to represent the performance of our suggested methodology, and practical business implications for this methodology are also provided. We believe that the change detection problem and the suggested methodology will become increasingly important as more data mining applications are implemented.     

Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District,Vietnam

Shrimp culture is a sector of aquaculture that has a high potential for poverty alleviation and rural development in Vietnam. However, the development of this activity induces changes that potentially have negative impacts on the environment, one of which is wetland deterioration. This paper describes the use of a proposed change detection methodology in the assessment of mangrove forest alterations caused by aquaculture development, as well as the effectiveness of the measures taken to mitigate deforestation in the district of Giao Thuy, Vietnam, between 1986, 1992 and 2001. Geometric and radiometric corrections were applied to Landsat images prior to identifying changes through comparison of unsupervised classifications. Changes were afterwards validated using a thresholding method based on Tasselled Cap feature image differencing and a rule‐based feature selection matrix. The matrix is used to identify the feature that is most efficient at detecting the presence of change between given land‐cover classes. The proposed approach aims to minimize commission errors in the post‐classification change detection process. The results suggest that 63% of mangrove areas apparent in 1986 had been replaced by shrimp ponds in 2001. Between 1986 and 1992, 440 ha of adult mangrove trees had disappeared, whereas the mangrove extent increased by 441 ha between 1992 and 2001. This recovery is attributed to reforestation projects and conservation efforts that promoted natural regeneration.     

对光照、阴影和反光具有鲁棒性的变化检测算法及实现

变化检测算法研究就是要发展一种能实现目标自动检测的算法,实现对视频图像序列的初级分析,稳定可靠地识别目标区域,实现运动图像分割,它是实现目标的识别、跟踪和报警等高层次的自动视频监视应用的重要基础,也是计算机视觉研究的一个重要领域。文章在研究了目前的多种变化检测算法的基础上,提出了一种基于线性相关的变化检测的算法。该算法以每一象素及其邻域组成的集合作为图像矢量来描述图像,用局部线性相关检测器来判断背景图像和当前图像的对应图像矢量是否线性相关,从而确定是否有变化发生。室内外的试验结果表明,基于线性相关的变化检测新算法对真实环境中的光照、阴影和反光具有较强鲁棒性,对噪声也有较强的抑制作用。该算法可准确地检测语义目标及其内部,且边缘光滑,检测目标与原图像中的目标准确地吻合,可以提高变化检测的精度,极大地增强自动视频监视系统对环境光照变化的适应能力。     

On evaluating stream learning algorithms

Most streaming decision models evolve continuously over time, run in resource-aware environments, and detect and react to changes in the environment generating data. One important issue, not yet convincingly addressed, is the design of experimental work to evaluate and compare decision models that evolve over time. This paper proposes a general framework for assessing predictive stream learning algorithms. We defend the use of prequential error with forgetting mechanisms to provide reliable error estimators. We prove that, in stationary data and for consistent learning algorithms, the holdout estimator, the prequential error and the prequential error estimated over a sliding window or using fading factors, all converge to the Bayes error. The use of prequential error with forgetting mechanisms reveals to be advantageous in assessing performance and in comparing stream learning algorithms. It is also worthwhile to use the proposed methods for hypothesis testing and for change detection. In a set of experiments in drift scenarios, we evaluate the ability of a standard change detection algorithm to detect change using three prequential error estimators. These experiments point out that the use of forgetting mechanisms (sliding windows or fading factors) are required for fast and efficient change detection. In comparison to sliding windows, fading factors are faster and memoryless, both important requirements for streaming applications. Overall, this paper is a contribution to a discussion on best practice for performance assessment when learning is a continuous process, and the decision models are dynamic and evolve over time.