遥感影像变化检测方法探讨

利用同一区域不同时相的遥感影像进行变化检测的应用一直是遥感应用研究的热点之一.本文对遥感变化检测技术的研究现状和新进展进行了一定的归纳和总结,对以往分类方法进行了介绍,分别对基于像素、基于对象以及结合特定理论的变化检测方法进行了重点讨论,并对每种方法涉及的理论和最新研究进展进行了评述.     

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。     

半监督离散势理论在遥感影像变化检测中的应用

随着遥感技术的发展,遥感影像变化检测作为一种有效的技术手段,在环境监测、灾害救援等领域发挥了重要作用。然而地物复杂、标记困难等问题导致有效的变化检测存在一定的困难。本文提出了一种基于半监督离散势理论的遥感影像变化检测方法。该方法首先采用一种新的标记样本点的方法得到训练集,然后利用KNN方法构造复杂网络,最后对复杂网络中经典Wu-Huberman算法进行改进并划分网络。所得到的两个社团结构恰好对应了变化部分和不变部分。试验结果表明,基于半监督离散势理论的变化检测方法具有良好的变化检测性能。     

Updating land cover automatically based on change detection using satellite images: case study of national forests in Southern California

Observing dynamic change patterns and higher-order complexities from remotely sensed images is warranted, but the main challenges include image inconsistency, plant phenological differences, weather variations, and difficulties of incorporating natural conditions into automatic image processing. In this study, we proposed a new algorithm and demonstrated it by producing 2002–2008 and 2010 land-cover maps in heterogeneous Southern California based on an existing 2009 land-cover map. The new algorithm improves the baseline land-cover map quality by discarding potential bad land-cover pixels and dividing each land-cover type into several subclasses. Time series Landsat images were used to detect changed and unchanged areas between baseline year and target year t. Subsequently, for each individual year t, each pixel that was identified as unchanged inherited the baseline classification. Otherwise, each pixel in the changed areas was classified by a similar surrogate majority classifier. The demonstration results in Southern California showed that the land-cover temporal pattern captured the observed successional stages of the ecosystem very well. The accuracy assessment had an overall classification accuracies ranging from 81% to 86% and overall kappa coefficients ranging from 0.79 to 0.83.     

Land change attribution based on Landsat time series and integration of ancillary disturbance data in the Athabasca oil sands region of Canada

The Alberta Oil Sands (AOS) is a unique area in Canada undergoing significant disturbance and recovery due to a variety of anthropogenic and natural factors. Accurately quantifying these changes in space and time is important for assessing ecosystem status and trends. In this research, we implemented an approach to combine Landsat time series for the period 1984–2012 with ancillary change datasets to derive detailed change attribution in the AOS. Detected changes were attributed to causes including fire, forest harvest, surface mining, insect damage, flooding, regeneration, and several generic change classes (abrupt/gradual, with/without regeneration) with accuracies ranging from 74% to 100% for classes that occurred frequently. Lower accuracies were found for the generic gradual change classes which accounted for less than 3% of the affected area. Timing of abrupt change events were generally well captured to within ±1 year. For gradual changes timing was less accurate and variable by change type. A land-cover time series was also created to provide information on “from-to” change. A basic accuracy assessment of the land cover showed it to be of moderate accuracy, approximately 69%. Results show that fire was the major cause of change in the region. As expected, surface mine development and related activities have increased since 2000. Insect damage has become a more significant agent of change in the region. Further investigation is required to determine if insect damage is greater than past historical events and to determine if industrial development is linked to the increasing trend observed.     

自适应无监督分类的PolSAR图像机场跑道区域快速检测

针对复杂PolSAR图像场景中机场目标区域检测问题,本文提出一种自适应无监督分类的机场目标快速检测方法,该方法首先对极化SAR相干矩阵分解提取PolSAR图像的特征值图,转化为超像素图实现图像去噪及降维。然后用SLIC超像素分割算法分割构造超像素。基于超像素图构建极化分类特征,并采用无监督的谱聚类方法提取出疑似机场跑道区域,其中的类别数确定利用VAT-DBE(Visual Assessment of cluster Tendency-Dark Block Extraction)算法获得。最后,在疑似区域内结合跑道结构特征进一步辨识检测出场景中的机场跑道区域。利用美国UAVSAR系统采集的多组全极化SAR实测数据对算法进行验证,并与两种已有的无监督跑道检测算法进行对比,实验结果表明,该算法能够快速准确检测出机场跑道区域,处理耗时可减小80%以上。具有更好的鲁棒性。     

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...     

Change detection of linear features in temporally spaced remotely sensed images using edge-based grid analysis

Automatic change detection of land cover features using high-resolution satellite images, is a challenging problem in the field of intelligent remote sensing data interpretation, and is becoming more and more effective for its applications viz. urban planning and monitoring, disaster assessment etc. In the present study, a change in detection approach based on the image morphology that analyses change in the local image grids is proposed. In this approach, edges from both the images are extracted and grid wise comparison is made by probabilistic thresholding and power spectral density analysis for identifying change area. One of the advantages of the proposed methodology is that the temporal images used in the change analysis need not be radiometrically corrected as analysis is based on edge extractions. The grid-based analysis further reduces the error, which might have been introduced by image mis-registration. The proposed methodology is validated by finding the temporal changes in the linear land cover features in parts of Kolkata city, India using three different image data-sets from LISS IV, Cartosat-1 and Google earth having varied spatial resolutions of 5.8 m, 2.5 m and about 1 m, respectively. The overall accuracy in identifying changes is found to be 64.82, 73.86 and 80.93% for LISS IV, Cartosat-1 and Google earth data-set, respectively.     

高分辨率遥感影像阴影检测与补偿系统的设计与实现

阴影检测与补偿涉及遥感影像的不确定性、算法复杂度高及提取自动化程度低等问题。基于Arc GIS Engine平台,结合Matlab和GDAL开发工具,根据构建的高分辨率遥感影像阴影检测和补偿算法设计了一体化的阴影检测与补偿系统。系统采用了数据分块读取、2%线性拉伸及DLL动态链接库等关键技术,解决了大数据量影像读取、影像不确定性及系统可扩展性等问题,实现了系统的集成和优化,提高了运行效率。测试结果表明,该系统在Quick Bird、资源三号(ZY-3)等高分辨率遥感影像的阴影检测与补偿中具有较高的精度和效率,可用于数据批处理。     

遥感影像变化检测在地形图快速修测中的应用

首先介绍地形网修测中常规判读地物地貌发生变化的方法,以及将变化检测技术应用于地形图修测中的必要性;其次介绍变化检测的基本原理、发展历史、国内外对变化检测概念的不同定义以及变化检测技术的特点和应用范围;紧接着从地形图快速修测这一客观需要出发,简明扼要地叙述变化检测方法的选择、变化检测的一般过程、变化检测结果在地形图修测中的应用,并结合变化检测的精度评估公式分析将变化检测技术应用于地形图修测工作中的实际效率;最后展望变化检测技术在地形图快速修测中的应用前景。     

自适应空间邻域分析和瑞利-高斯分布的多时相遥感影像变化检测

提出了一种基于自适应空间邻域分析和瑞利-高斯模型(Rayleigh-Gauss models, RGM)分布的多时相遥感影像自动变化检测方法。该方法把自适应空间邻域信息和改进的差值影像与比值影像乘积变换融合 法(improved multiplying transform fusion, IMTF)结合构造差异影像, 可以有效地抑制噪声和消除多时相影像之间配准误差的影响, 具有更强的鲁棒性。在对差异影像的分割处理中, 运用瑞利和高斯模型分别模拟变化类像元和非变化类像元的分布情况, 然后估计出两类像元的概率     

高分遥感影像与矢量数据结合的变化检测方法

针对高分辨率遥感影像与矢量数据配准套合之后存在的不一致性问题,该文探讨了多尺度分割算法获取同质像斑的方法。在此基础上,发展了一种基于最近邻分类算法的分类后处理变化检测方法。结果表明,通过多尺度分割算法能够获取"类内光谱相同"和"类间光谱相异"的像斑;分类后处理变化检测方法能够正确检测出80%以上的变化区域,在获取变化检测结果的同时能够获取变化像斑的类别,而且检测精度较高,具有较高的应用价值。     

环渤海滨海湿地时空格局变化遥感监测与分析

基于多期Landsat TM 等遥感数据, 采用目视解译和实地样点采集相互支撑的方法, 完成了环渤海 地区2000 年、2005 年及2008 年湿地提取和分类; 运用单一类型变化率模型、区域动态度模型和动态转移矩 阵, 揭示了环渤海湿地的时空格局、变化特点和驱动机制。研究显示环渤海地区的8 类湿地中, 属于人工湿 地类型的盐场和水库坑塘面积比例大且近8 年增长速度也最快, 年均分别增加205.52 km2 和146.10 km2, 滩 地和海涂减少最明显; 环渤海地区的三大流域中, 黄河流域湿地变化最显     

基于主成分分析与粒子群优化的遥感影像变化检测

针对变化检测中差异影像上变化阈值选择困难的问题,该文提出了使用粒子群优化算法自动地从差异影像上选择最优变化阈值的方法。该方法首先利用主成分分析法从影像中提取包含最大信息的主分量,利用主分量构建差异影像;在此基础上,使用高斯混合模型估算差异影像的初始阈值,最后通过粒子群算法计算出最优的变化阈值。实验结果表明,该方法能够迅速地获取变化阈值,避免了人工选择时繁琐重复的尝试,且检测结果精度较高。     

青藏高原湿地遥感监测与变化分析

青藏高原是中国湿地分布较为集中的地区之一,也是全球变化的敏感区.了解青藏高原湿地分布与变化对湿地保护和全球变化研究具有重要意义.基于Landsat 8 OLI (operation land imager)数据,使用面向对象分类方法和人工解译相结合的方式得到2016年青藏高原湿地分布数据,结合2008年湿地分类数据以及...     

Quantifying shoreline change in Funafuti Atoll,Tuvalu using a time series of Quickbird,Worldview and Landsat data

Funafuti Atoll, Tuvalu is located in the southwestern Pacific Ocean, which has experienced some of the highest rates of global sea-level rise over the past 60 years. Atoll islands are low-lying accumulations of reef-derived sediment that provide the only habitable land in Tuvalu, and are considered vulnerable to the myriad possible impacts of climate change, especially sea-level rise. This study examines the shoreline change of twenty-eight islands in Funafuti Atoll between 2005 and 2015 using 0.65 m QuickBird, 0.46 m WorldView-2, and 0.31 m WorldView-3 imagery using an image segmentation and decision tree classification. Shoreline change estimates are compared to previous study that used a visual interpretation approach. The feasibility of estimating island area with Landsat-8 Operational Land Imager (OLI) data is explored using CLASlite software. Results indicate a 0.13% (0.35 ha) decrease in net island area over the study time period, with 13 islands decreasing in area and 15 islands increasing in area. Substantial decreases in island area occurred on the islands of Fuagea, Tefala and Vasafua, which coincides with the timing of Cyclone Pam in March, 2015. Comparison between the WorldView-2 shoreline maps and those created from Landstat-8 indicate that the estimates tend to be in higher agreement for islands that have an area > 0.5 ha, a compact shape, and no built structures. Ten islands had > 90% agreement, with percent disagreements ranging from 2.78 to 100%. The methods and results of this study speak to the potential of automated EoV shoreline monitoring through segmentation and classification tree approach, which would reduce down data processing and analysis time. With the growing constellation of high and medium spatial resolution satellite-based sensors and the development of semi or fully automated image processing technology, it is now possible to remotely assess the short and medium-term shoreline dynamics on dynamic atolls. Landsat estimates were reasonably matched to those derived from fine resolution imagery, with some caveats about island size and shape.     

Characterizing wetland dynamics: a post-classification change detection analysis of the East Kolkata Wetlands using open source satellite data

The East Kolkata Wetlands is a unique resource recovery system. The Ramsar Convention recognized it as a ‘Wetland of International Importance’ in August 2002. However, the long-term resource exploitation and land use changes in the dynamic ecosystem have resulted in non-linear environmental responses. This is an attempt using open source remote sensing datasets to capture the spatio-temporal transformation of the wetland resulting from various anthropogenic activities. Landsat MSS and TM imageries of 1973, 1980, 1989, 2001 and 2010 were classified using Maximum Likelihood Classifier to monitor the wetland change; however, to study wetland dynamics, the post-classification wetland change detection maps have been generated for two temporal phases, i.e. 1973–1989 and 1989–2010. This study finds that the area under wetlands has reduced comprehensively in the past 40 years due to the conversion of wetlands into various other uses such as urban expansion of the Kolkata metropolitan city.     

Implications of management strategies and vegetation change in the Mount St. Helens blast zone

The 1980 eruptions of Mount St. Helens provided an excellent opportunity for scientists to investigate the recovery of vegetation communities following a major geologic disturbance. An important and often overlooked aspect in these studies is the human factor in recovery processes, and specifically, the different management approaches taken towards re-establishment of vegetation on lands under the control of various owners. This study examines vegetation changes throughout the 1980 blast zone using a time series of Landsat-derived Normalized Difference Vegetation Index (NDVI) images and change detection methods to assess the changes over 25 years, from 1980 to 2005, as a function of human management combined with ecological factors. This long-term tracking of change indicates that differences in the speed of vegetation re-establishment and consequent rates of change substantially reflect human involvement and varying management strategies.     

黄河三角洲典型地区土地利用变化遥感监测及驱动力分析

黄河三角洲是我国重要的后备土地资源开发利用区,是土地利用、覆被变化研究的热点区域。本文选择黄河三角洲核心区域垦利县为研究区,采用2001、2005、2011和2014年的卫星遥感数据,基于RS和GIS技术,在ENVI软件支持下,利用监督分类方法,提取了研究区各时相土地利用类型面积及分布信息,研究了该区土地利用时空变化特征与规律,并结合研究时段内的自然和社会经济因素,对研究区土地利用变化驱动力进行了分析。研究结果表明:耕地、盐荒地和滩涂是该区土地利用的主要类型;研究时段内,垦利县土地利用发生了显著变化,陆地面积总体增加,耕地、林草地呈现减少趋势。耕地是减少速度最快、减少面积最大的土地利用类型,建设用地、盐荒地、滩涂则处于持续增长状态。气候水文因素是主要的自然驱动力,而人类的开发建设活动是主要人为驱动力。研究结果对于该区的土地资源合理利用及经济社会可持续发展具有积极意义。     

Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands,India, using IRS LISS III satellite images

A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.