基于多时相TM影像的城市边缘区划分及其变化监测

城市边缘区作为城市和农村之间的过渡地带,是城市扩张过程中土地利用变化最为活跃的部分。在城市边缘区,城市用地类型与其他的土地利用类型,比如耕地、林地、牧草地和水域等混合在一起,并且这些非城市用地类型随着城市化的进程很快转换为城市用地。城市边缘区被定义为城市内边界和外边界之间的环状区域,内边界分离城市核心区与城市边缘区,外边界分离城市边缘区与农村腹地。本研究采用一种新的方法来对城市边缘区进行界定,以及对其动态变化进行监测研究。通过多时相遥感数据的分类,提取城市及其周边的土地利用信息,并对其空间结构模式用地理景观指标进行定量的描述,最后借助空间聚类获取边界阈值来划分城市边缘区并对其变化进行监测。     

Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta

The Pearl River Delta is experiencing very fast urban growth in recent years which has caused rapid loss of the valuable agricultural land in this fertile region. There is a great need to monitor the rapid urban expansion using remote sensing for urban planning and management purposes. However, it has been well recognized that there is significant over-estimation of land use change in using multi-temporal images for change detection because of inadequate creation of classification signatures. This paper presents a principal component analysis of stacked multi-temporal images method to reduce such errors. The study demonstrates that this method can reduce errors in change detection using multitemporal images and provide a very useful way in monitoring rapid land use changes and urban expansion in the Pearl River Delta and other parts of the world.     

基于格网划分的城市区域土地利用景观格局时空变化遥感监测

城市化引起的土地利用变化已成为城市问题研究热点。多时相遥感变化检测能够监测到土地利用变化的数量,被广泛地用来进行城市扩张研究。对于城市化引起的城市空间结构变化,最新研究引入景观格局分析法,大量涌现的景观指标为景观格局定量化表达提供了基础。目前对于城市化过程中景观格局时空变化的描述过于笼统,一般是对整个研究区域提取全局景观格局及其时间变化。通过提出一种基于网格划分的景观格局提取与时空变化检测方法,并运用此方法研究了北京市城市化进程中景观格局的时空变化。结果表明:基于网格划分的景观格局变化检测方法能够检测出城市空间结构变化的数量、位置和模式,为理解城市扩张行为以及城市扩张建模提供了相比较于遥感土地利用变化检测之外的另一种知识。     

Spatio-temporal pattern of urban land cover evolvement with urban renewal and expansion in Shanghai based on mixed-pixel classification for remote sensing imagery

Research into pixel unmixing in remote sensing imagery led to the development of soft classification methods. In this article, we propose a possibilistic c repulsive medoids (PCRMdd) clustering algorithm which attempts to find c repulsive medoids as a minimal solution of a particular objective function. The PCRMdd algorithm is applied to predict the proportion of each land use class within a single pixel, and generate a set of endmember fraction images. The clustering results obtained on multi-temporal Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper plus (ETM+) images of Shanghai city in China reveal the spatio-temporal pattern of Shanghai land use evolvement and urban land spatial sprawl in course of urbanization from 1989 to 2002. The spatial pattern of land use transformation with urban renewal and expansion indicates the urban land use structure is gradually optimized during vigorous urban renewal and large-scale development of Pudong area, which will have an active influence on improving urban space landscape and enhancing the quality of the ecological environment. In addition, accuracy analysis demonstrates that PCRMdd represents a robust and effective tool for mixed-pixel classification on remote sensing imagery to obtain reliable soft classification results and endmember spectral information in a noisy environment.     

Accuracy analysis of remote sensing change detection by rule-based rationality evaluation with post-classification comparison

Accuracy assessment for remote sensing classification is commonly based on using an error matrix, or confusion table, which needs reference, or ‘ground truthing’, data to support. When undertaking change detection using numerous multi-temporal images, it is often difficult to make the accuracy assessment by the ‘traditional’ method, which typically requires simultaneous collection of reference data. In this study, we propose a new approach by arguing change rationality with post-classification comparison. Multi-temporal Landsat TM images were classified for land use in an urban fringe area of Beijing, China and the post-classification comparison of these classified images shows change trajectories through the time series. These change trajectories were then analysed by assessing their rationality against a set of logical rules to separate cases of ‘real land use change’ and possible classification errors. The analysis results show that the overall accuracy for land use change in the urban fringe area was 86%, with a fuzziness of 7%. Although it is argued that the uncertainty still exists on classification accuracy assessed by this method, it nevertheless provides an alternative approach for more reasonable assessment when ideal simultaneous ‘ground truthing’ is not available.     

Using random walker for knowledge transfer in classifying multi-temporal VHR images

Acquiring land cover types from very high resolution (VHR) images is of great significance to many applications and has been intensively studied for many years. The difficulties in image classification and the high frequencies of remote sensing image acquisition make it urgent to develop efficient knowledge transfer approaches for understanding multi-temporal VHR images. This letter proposed a knowledge transfer approach that uses the label information of the existing VHR images to classify multi-temporal images. The approach was implemented in three steps: object-based change detection, knowledge transfer of label information, and random walker (RW) classification. The proposed approach was tested by two datasets with each having two temporal images acquired on the same geographical areas. The experimental results showed that the proposed approach outperformed the support vector machine (SVM) algorithm in classifying multi-temporal images and can reduce the influence of spectral confusions on image classification.     

Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data

Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.     

长春市城市扩张及其微气候响应

针对目前城市化和热岛效应研究多为单一定性评价和半定量化研究,缺乏耦合分析的问题,提出了一种可行的定量耦合分析方法。分析了长春市建设用地扩张和热岛效应的时空演变特征及其关系。首先收集遥感数据,采用归一化差分复合指数阈值分类法提取建设用地,之后采用辐射传输法反演地表温度,以此为基础计算扩张强度、热岛强度等指数,进而将城市扩张与微气候变化进行相关性分析。结果表明,长春市建设用地1990—2016年间增加了608.85 km^2,2000年后陡增,扩张模式由面状扩张变为沿道路辐射状扩张、飞地式扩张。城市扩张初期,相对热岛效应显著,随着建设用地的扩大,热岛面积增大,但是强度下降。相关性分析表明,单位格网内城市用地所占面积高于40%时地表温度上升明显。     

线性模型的遥感图像时空融合

目的 时空融合是解决当前传感器无法兼顾遥感图像的空间分辨率和时间分辨率的有效方法。在只有一对精细-粗略图像作为先验的条件下，当前的时空融合算法在预测地物变化时并不能取得令人满意的结果。针对这个问题，本文提出一种基于线性模型的遥感图像时空融合算法。方法 使用线性关系表示图像间的时间模型，并假设时间模型与传感器无关。通过分析图像时间变化的客观规律，对模型进行全局和局部约束。此外引入一种多时相的相似像素搜寻策略，更灵活地选取相似像素，消除了传统算法存在的模块效应。结果 在两个数据集上与STARFM（spatial and temporal adaptive reflectance fusion model）算法和FSDAF（flexible spatiotemporal data fusion）算法进行比较，实验结果表明，在主要发生物候变化的第1个数据集，本文方法的相关系数CC（correlation coefficient）分别提升了0.25%和0.28%，峰值信噪比PSNR（peak signal-to-noise ratio）分别提升了0.153 1 dB和1.379 dB，均方根误差RMSE（root mean squared error）分别降低了0.05%和0.69%，结构相似性SSIM（structural similarity）分别提升了0.79%和2.3%。在发生剧烈地物变化的第2个数据集，本文方法的相关系数分别提升了6.64%和3.26%，峰值信噪比分别提升了2.086 0 dB和2.510 7 dB，均方根误差分别降低了1.45%和2.08%，结构相似性分别提升了11.76%和11.2%。结论 本文方法根据时间变化的特点，对时间模型进行优化，同时采用更加灵活的相似像素搜寻策略，收到了很好的效果，提升了融合结果的准确性。     

基于多时相协同变化检测的耕地撂荒遥感监测

针对地表覆被复杂、地块破碎等原因导致的撂荒地提取精度较低问题,提出一种基于多时相协同变化检测的耕地撂荒信息提取方法。以河北省石家庄市鹿泉区为研究区,采用Sentinel?2A和Landsat 7多光谱影像,在野外样本的支持下,分析耕地各种覆盖类型的归一化植被指数（Normalized Difference Vegetation Index,NDVI）季相变化规律,以季节性撂荒、常年性撂荒、冬小麦、多年生园地为分类体系,构建多时相协同变化检测模型,开展研究区耕地撂荒状态遥感监测。研究结果表明：基于Sentinel?2A影像的季节性撂荒和常年撂荒耕地的分类精度分别为95.83%和96.55%;基于Landsat 7影像的季节性撂荒和常年撂荒耕地的分类精度分别为91.67%和93.10%;2019年鹿泉区季节性撂荒占耕地面积的4.7%,常年撂荒耕地占7.1%。利用该方法能够快速、准确地获取研究区耕地空间分布、面积等信息,对于不同分辨率的影像均具有较好的撂荒地提取精度。     

A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis

Rapid urban growth in developing countries is causing a great number of urban planning problems. To control and analyse this growth, new and better methods for urban land use mapping are needed. This article proposes a new method for urban land-use mapping, which integrates spatial metrics and texture analysis in an object-based image analysis classification. A high-resolution satellite image was used to generate spatial and texture metrics from the machine learning algorithm of Random Forests land-cover classification. The most meaningful spatial indices were selected by visual inspection and then combined with the image and texture values to generate the classification. The proposed method for land-use mapping was tested using a 10-fold cross-validation scheme, achieving an overall accuracy of 92.3% and a kappa coefficient of 0.896. These steps produced an accurate model of urban land use, without the use of any census or ancillary data, and suggest that the combined use of spatial metrics and texture is promising for urban land-use mapping in developing countries. The maps produced can provide the land-use data needed by urban planners for effective planning in developing countries.     

Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015

To analyse changes in human settlement in Shenzhen City during the past three decades, changes in land use/land cover (LULC) and urban expansion were investigated based on multi-temporal Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images. Using C4.5-based AdaBoost, a hierarchical classification method was developed to extract specific classes with high accuracy by combining a specific number of base-classifier decisions. Along with a classification post-processing approach, the classification accuracy was greatly improved. The statistical analysis of LULC changes from 1988 to 2015 shows that built-up areas have increased 6.4-fold, whereas cultivated land and forest continually decreased because of rapid urbanization. Urban expansion driven by human activities has considerably affected the landscape change of Shenzhen. The urban-expansion pattern of Shenzhen is a mixture of three urban-expansion patterns. Among these patterns, traffic-driven urban expansion has been the main form of urban expansion for some time, especially in the Non-Special Economic Zone. In addition, by taking 8 to 10 year periods as time intervals, urban expansion in Shenzhen was divided into three stages: the early-age urbanization stage (1988–1996), the rapid urbanization stage (1996–2005), and the intensive urbanization stage (2005–2015). For different stages, the state of urban expansion is different. In long-term LULC dynamic monitoring and urban-expansion detection, it was possible to obtain 11 LULC maps, which took 2 to 4 years as a research interval. With regard to the short research periods, LULC changes and urban expansion were investigated in detail.     

Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS

We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47?km². The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.     

一种结合空间与光谱信息的改进CVA变化检测方法

基于变化向量分析（CVA）的变化检测方法通过直接比较像素差异,能够快速提取多时相影像间的变化信息。尽管如此,由于忽略了像素领域的空间上下文信息及波段之间的差异性和互补性,导致检测结果中难以消除噪声等因素产生的“伪变化”。为此提出了一种结合空间和光谱信息的改进CVA方法。首先,采用主成分分析法对影像进行增强,继而通过构建一种新的多方向差分描述子来提取中心像素的空间上下文信息;在此基础上,提出一种基于相关性的加权融合策略,获得统一的变化强度差分影像;最后,采用EM算法求得变化像素的阈值,继而得到二值检测结果。实验结果表明：所提出的算法能够有效应对“伪变化”的干扰,显著提高变化检测的精度及可靠性。     

一种结合空间与光谱信息的改进CVA变化检测方法

基于变化向量分析（CVA）的变化检测方法通过直接比较像素差异，能够快速提取多时相影像间的变化信息。尽管如此，由于忽略了像素领域的空间上下文信息及波段之间的差异性和互补性，导致检测结果中难以消除噪声等因素产生的“伪变化”。为此提出了一种结合空间和光谱信息的改进CVA方法。首先，采用主成分分析法对影像进行增强，继而通过构建一种新的多方向差分描述子来提取中心像素的空间上下文信息；在此基础上，提出一种基于相关性的加权融合策略，获得统一的变化强度差分影像；最后，采用EM算法求得变化像素的阈值，继而得到二值检测结果。实验结果表明：所提出的算法能够有效应对“伪变化”的干扰，显著提高变化检测的精度及可靠性。     

结合MNF变换和Canny算子的遥感影像变化检测

针对大面积区域的多时相遥感影像变化检测的需求，提出了一种基于最小噪声分离（MNF）的Canny边缘检测提取影像变化信息的检测方法。对多时相影像采用多种变换组合成具有多维波段信息的影像，采用最小噪声分离法分离噪声并得到单波段差异图，通过Canny边缘检测法计算梯度幅值，采用高低双阈值法细化边缘，从而提取差异图变化边缘，有效突出了变化信息。以1995年和2003年加扎勒河的两期遥感影像为例，利用两时相影像进行土地覆被变化检测。实验结果表明，该方法适用于监测大面积区域内地物的突变情况。在数据基础上进行最小噪声分离可以有效解决传统Canny边缘检测提取边缘时造成的伪边缘现象，同时采用高低双阈值法有效去除伪边缘点，从而获得更加精确、直观的变化检测效果，在自然地理变化监测、地理国情灾害监测等有很好的应用价值。     

面向对象的土地利用变化检测方法研究

利用分类后比较法进行土地利用变化检测时,常用的自动分类方法只能利用遥感数据的光谱信息,分类精度较低,本文在此基础上将面向对象的分类方法引入到变化检测中。该方法综合利用遥感数据光谱信息、纹理特征、拓扑关系和专题信息,在进行多尺度分割获取对象后的基础上,通过对对象的目视识别选择样本来进行分类。利用面向对象的方法成功检测出了所选取的试验区十年间的土地利用变化信息,得到了较为满意的结果,为土地资源可持续利用提供了依据。     

基于智能CASE库的多时相SAR影像分类方法-土地利用及变化监测

针对SAR影像分类,提出了一种基于智能案例(CASE)库多时相SAR影像分类方法。该方法主要分为4部分:SAR影像预处理;智能CASE的建构;基于CASE相似度匹配的SAR影像分类;分类后处理。在智能CASE建构期间,引入时空分析技术去除“伪”CASE,从而保证了CASE库中CASE信息的可靠性。接着,在基于CASE匹配的SAR影像分类过程中,采用分层相似度评价的方法,消除CASE特征相互之间的混叠效应。最后,采用面向对象的方法进行影像分类后处理。该方法有效地考虑了分类地块的形状因子,使分类结果更精确、更符合逻辑性。以2000年(4景,包含4个季度)和2004年(3景,包含3个季度)的多时相SAR影像作为实验数据,结果表明,使用我们提出的方法能达到较好的SAR影像分类结果,分类总体精度达到85%～90%,这为利用多时相SAR影像实施土地利用和变化监测(Land Use and Land Cover Change,LULC)奠定了良好基础。     

(2+1)D多时空信息融合模型及在行为识别的应用

针对常规的卷积神经网络时空感受野尺度单一,难以提取视频中多变的时空信息的问题,利用（2+1）D模型将时间信息和空间信息在一定程度上解耦的特性,提出了（2+1）D多时空信息融合的卷积残差神经网络,并用于人体行为识别.该模型以3×3空间感受野为主,1×1空间感受野为辅,与3种不同时域感受野交叉组合构建了6种不同尺度的时空感受野.提出的多时空感受野融合模型能够同时获取不同尺度的时空信息,提取更丰富的人体行为特征,因此能够更有效识别不同时间周期、不同动作幅度的人体行为.另外提出了一种视频时序扩充方法,该方法能够同时在空间信息和时间序列扩充视频数据集,丰富训练样本.提出的方法在公共视频人体行为数据集UCF101和HMDB51上子视频的识别率超过或接近最新的视频行为识别方法.