结合遥感数据与地统计学方法的海岸线超分辨率制图

使用黄河三角洲海岸Landsat卫星遥感数据,基于研究区域低分辨率6波段的海陆类型软分类结果及其变差函数,以高分辨率8波段的指示变差函数为精细尺度先验信息模型,采用数据探索性分析、协同指示克里格和序贯指示协同模拟技术,生成海陆类型发生概率模拟图像,通过等值线法提取海岸线空间分布特征。实验表明,基于地统计学方法的超分辨率制图技术在低分辨率遥感数据中融合高分辨率空间结构先验模型,可以较好表达精细尺度上的海岸线空间分布特征,同时保持原始数据的海陆类型组分信息及其空间结构特征。地统计学方法集成多尺度乃至多源空间信息的潜力通过海岸线超分辨率制图形式得到展示。     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

改进的P-SVM支持向量机与遥感数据分类

本文介绍了将P-SVM算法引入多光谱/高分辨率遥感数据的分类, 并且展示了卫星ASTER和航空ADS40数字影像分类的技术过程和结果验证。结果表明：P-SVM方法的分类精度不低于SVM, 并减少了时耗。     

Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model

Cloud cover is generally present in remotely sensed images, which limits the potential of the images for ground information extraction. Therefore, removing the clouds and recovering the ground information for the cloud-contaminated images is often necessary in many applications. In this paper, an effective method based on similar pixel replacement is developed to solve this task. A missing pixel is filled using an appropriate similar pixel within the remaining region of the target image. A multitemporal image is used as the guidance to locate the similar pixels. A pixel-offset based spatio-temporal Markov random fields (MRF) global function is built to find the most suitable similar pixel. The proposed method was tested on MODIS and Landsat images and their land surface temperature products, and the experiments verify that the proposed method can achieve highly accurate results and is effective at dealing with the obvious atmospheric and seasonal differences between multitemporal images.     

An improved algorithm for supervised fuzzyC-means clustering of remotely sensed data

This paper describes an improved algorithm for fuzzyc-means clustering of remotely sensed data, by which the degree of fuzziness of the resultant classification is decreased as comparing with that by a conventional algorithm: that is, the classification accuracy is increased. This is achieved by incorporating covariance matrices at the level of individual classes rather than assuming a global one. Empirical results from a fuzzy classification of an Edinburgh suburban land cover confirmed the improved performance of the new algorithm for fuzzyc-means clustering, in particular when fuzziness is also accommodated in the assumed reference data.     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.     

高分辨率遥感影像配准与变化检测一体化处理

为了克服高分辨率遥感影像配准与变化检测作为单独环节处理的局限,该文提出了一种基于变分理论的配准与变化检测一体化处理方法。该方法将配准误差作为一种光谱变化决策因子,变化信息以权值的方式迭代反馈给变分配准模型的解算过程。为了更准确地检测建筑物这个特定目标的真实变化,该文采用多尺度最大形态学轮廓建筑物检测指数的差异作为另外一个决策因子。最后将配准误差反映的变化和建筑物检测指数的差异这两个决策因子在D-S证据理论框架下建立概率模型进行融合处理,进而得到建筑物的变化检测结果。该文选取WorldView-2数据进行实验,实验结果表明,一体化处理思路可以有效地解决单独处理的局限,从根本上解决配准误差对变化检测结果的影响以及由于变化而使配准精度降低的问题,进而提高配准和变化检测的质量。     

高光谱与高空间分辨率遥感数据的亚热带森林生物量反演

精确估算森林生物量对全球碳平衡以及气候变化的研究有重要意义。以亚热带天然次生林为研究对象,借助地面实测样地数据,通过对机载LiCHy(LiDAR,CCD and Hyperspectral)传感器同时获取的高光谱和高空间分辨率数据进行信息提取和数据融合,建模反演森林生物量。首先通过面向对象分割方法进行单木冠幅提取,然后融合从高光谱数据提取的光谱特征变量和从高空间分辨率数据提取的单木冠幅统计变量,构建多元回归模型估算地上、地下生物量,最后利用地面实测生物量经交叉验证评价模型精度。结果表明,综合模型的精度(R~2为0.54—0.62)高于高光谱模型(R~2为0.48—0.57);在高光谱模型中地上生物量模型精度(R~2为0.57)高于地下生物量模型(R~2为0.48);在综合模型中地上生物量模型精度(R~2为0.62)同样高于地下生物量模型(R~2为0.54)。交叉验证结果表明,与仅使用高光谱数据(单一数据源)相比,通过集成高光谱和高空间分辨率数据的生物量反演效果有所提升,可以更加有效地估算亚热带森林生物量。     

Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data

This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status.For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out.The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation.HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.     

热红外地表温度遥感反演方法研究进展

地表温度是表征地表过程变化的一个非常重要的特征物理量,是地表—大气能量交换的直接驱动因子,广泛地用于地表能量平衡、气候变化和资源环境监测等研究领域。本文系统地评述了热红外地表温度遥感反演方法,包括单通道算法、多通道算法、多角度算法、多时相算法和高光谱反演算法。回顾了地表温度反演的基础理论和方法;并在此基础上,进一步综述了地表温度遥感反演的验证方法,以及地表温度的时间和角度归一化方法;最后对未来提高地表温度反演精度的研究方向提出了建议。     

大坝监测资料的整编分析方法

结合某大坝监测资料,采用整编分析的图示方法对大坝施工和运行过程进行监测。主要描述大坝典型断面各监测点的三维变形状况及主坝区三维变形分布规律。实践表明：图示方法对变形规律的描述直观、简洁,能够整体反映建筑物的变形状况。     

变形监测数据管理与分析系统设计与实现

变形监测具有周期性、监测时间长、监测点多等特点,导致监测数据量大,故对海量变形监测数据的管理与分析就显得十分重要。为了实现对海量变形监测数据的高效管理与分析,文中采用C#语言,以SQL 2008为数据库,编写变形监测数据管理与分析系统,并通过工程实例验证系统的高效与可行性。     

Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data

Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat thematic mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult.     

中国环境遥感监测技术进展及若干前沿问题

新时期中国环境污染防治和生态文明建设的迫切需求催生了环境遥感监测技术的迅速发展,20多年来,中国环境遥感监测从无到有,从弱到强,逐渐进入到国家生态环境保护的主战场,并成为国家环境管理决策不可或缺的重要技术手段,发挥了关键性支撑作用。面对实行最严格的生态环境保护制度、科学治污、精准治污、依法治污的新形势和新要求,中国环境遥感监测面临前所未有的机遇和挑战。本文首先以环境监测卫星建设和应用为主线,回顾了中国环境遥感监测工作历程;然后从典型关键技术研究概况入手,总结了水环境遥感监测、大气环境监测、生态环境监测关键技术的进展;最后,结合高性能对地观测、大数据等先进技术的发展,对后续环境卫星、生态环境问题遥感主动发现、环境遥感监测大数据、基于深度学习的环境遥感反演等前沿问题进行了探讨,指出了中国环境遥感急需解决的问题和发展方向,并对相关技术的发展和应用前景进行了分析和展望。可以看到,在国家有关部门的高度重视和作者所带领团队及大量相关研究人员的共同努力下,中国已初步建立了环境遥感监测技术体系,并形成了业务化应用能力,以高分辨率探测为核心的新一代环境监测卫星正在得到快速发展,环境专用卫星载荷的技术性能将得到大幅提升,同时,环境遥感机理研究正得到进一步加强,环境遥感监测的精度和效率将得到进一步提升,中国环境遥感监测正在与人工智能、大数据等新技术加速融合,从以数理建模为核心的模型驱动时代进入到以智能感知为特征的数据驱动时代,由此将催生新的环境遥感应用场景和大数据产品不断涌现,推动环境遥感监测向智能感知、智能预警、智能决策、智能服务的方向发展。     

沉降监测数据分析系统的设计与实现

基于沉降监测数据处理的方法,主要是非等间距GM(1,1)预测原理,利用软件工程的思想,开发实现了沉降监测数据分析系统,为沉降信息系统的发展与应用提供经验,进而为今后类似系统的开发提供参考.     

GPS监测网数据质量分析

为保证GPS监测网络满足监测精度要求，本文提出了GPS外业数据采集，基线解算应采取的措施及数据处理方法，并对两期观测数据及处理结果进行质量分析，以保证变形分析的可靠性。     

Integrating Web-based GIS and image processing tools for environmental monitoring and natural resource management

The combined powers of Web-based geographic information systems (GIS) and on-line remote sensing tools can significantly reduce the high cost and labor associated with environmental monitoring and natural resource management. This paper introduces an integrated Web-based GIS architecture by combining three levels of geographic information services (GIServices): data archive, information display, and spatial analysis. A prototype Web site, WGAT (Web-based GIS and Analytic Tools), has been developed to provide easy access of geospatial information and to facilitate Web-based image analysis and change detection capabilities for natural resource managers and regional park rangers. The Web-based integration framework emphasizes user-oriented services, distributed network environments, metadata standards, communication protocols, client/server computation, and ubiquitous access.This paper forms a portion of the Integrated Mobile GIS and Wireless Image Web Services for Environmental Monitoring and Management project supported by NASAs Affiliated Research Center (ARC) at San Diego State University. Funding by the NASA ARC program and matching funds from the San Diego State University Foundation are acknowledged and greatly appreciated. The author wishes to thank John Kaiser, the ARC program coordinator, and Dr. Douglas Stow, the ARC program Principle Investigator, for their coordination efforts on this project. Java programming was provided by Liang Guo, a geography graduate student at San Diego State University.     

多维线性内插在变形监测数据分析中的应用

针对变形监测中由于形变体的变形是多因素同时作用,且各个变形因子对变形体的作用非线性,导致难以准确分析变形因子与形变量的函数关系的问题,该文提出一种多维空间线性内插的数据分析方法,实验结果表明:通过该方法不仅可以较准确分析出每个变形因子与形变量的关系,而且可以探测出形变量的跳变,为其他经验模型分析方法提供参考。     

国家环境遥感监测体系研究与实现

随着国家空间基础设施的建设和发展,中国遥感监测体系研究与建设取得了重大突破,从无到有形成了国家环境遥感监测能力,并业务化实现了主要环境遥感监测要素产品的生产与服务,为新时期环境管理工作提供了有力支撑。在分析国内外环境遥感发展及中国环境遥感监测需求的基础上,阐述了中国环境监测体系建设的必要性,研究了国家环境遥感监测体系的建立与业务化运行,提出了中国环境遥感监测体系发展思路与下一步重点任务。     

Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring

Monitoring Earth dynamics using current and future satellites is one of the most important objectives of the remote sensing community. The exploitation of image time series from sensors with different characteristics provides new opportunities to increase the knowledge about environmental changes and to support many operational applications. This paper presents an image fusion approach based on multiresolution and multisensor regularized spatial unmixing. The approach yields a composite image with the spatial resolution of the high spatial resolution image while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested using images from Landsat/TM and ENVISAT/MERIS instruments, but is general enough to be applied to other sensor pairs. The potential of the proposed spatial unmixing approach is illustrated in an agricultural monitoring application where Landsat temporal profiles from images acquired over Albacete, Spain, in 2004 and 2009 are complemented with MERIS fused images. The resulting spatial resolution from Landsat allows monitoring small and medium size crops at the required scale while the fine spectral and temporal resolution from MERIS allow a more accurate determination of the crop type and phenology as well as capturing rapidly varying land-cover changes.