A new approach for tropical forest area monitoring using multiple spatial resolution satellite sensor imagery

Tropical forest deforestation is a major concern at global, regional, and local scales. Recent advances have demonstrated the feasibility of using coarse spatial resolution remote sensing imagery to assess tropical forest over large areas. To date, few methods have been developed for the estimation of change over large areas. A change detection method based on a combination of NOAAAVHRR, Landsat TM, and SPOT-HRV data is evaluated on a study site in Vietnam, Southeast Asia. Changes detected with fine spatial resolution imagery are related to AVHRR-derived forest class proportions and fragmentation patterns.     

Les systèmes légers de télédétection aéroportée pour les sciences de la Terre

Satellite remote sensing, a powerful monitoring tool, often lacks the high spatial resolution and operational flexibility needed for research in Earth Sciences. In order to highlight the concept of linking ground data and remote sensing imagery, a review of tremendous recent advances in low-altitude remote sensing, as well as in supporting systems such as light airborne platforms is presented. Airborne digital photography and videography provide near real-time data at the local and regional scales. The integration of portable multi-spectral digital sensors, navigation systems and small slow-flying aircraft creates a modern do-it-yourself data acquisition tool for everyday field work and ground truthing of satellite imagery. This approach closes the existing gap between the advanced spaceborne remote sensing methods and the precise but inefficient ground studies. It is particularly advantageous for geomorphology and Earth Sciences.     

卫星遥感及图像处理平台发展

航天科技是国家综合国力和科技实力的重要体现,而卫星遥感则是航天科技转化为生产力最直接、最现实的途径之一。遥感数据获取与分发、数据处理与信息提取是卫星遥感应用的两个基本步骤。随着国家民用空间基础设施规划中的遥感卫星体系稳步推进,以及商业卫星遥感的蓬勃发展,我国的卫星遥感数据获取能力呈现质量齐升之势。但同时,作为卫星遥感应用的基础设施和关键工具,遥感图像处理系统平台逐渐成为制约自主卫星数据应用和空间信息业务发展的重要因素之一。本文围绕卫星遥感对地观测主题,从卫星遥感数据获取能力、卫星遥感数据处理系统平台两方面,对国内外现状进行综述,在此基础上分析了卫星遥感的发展趋势。     

Performance of different spectral and textural aerial photograph features in multi-source forest inventory

Most multi-source forest inventory (MSFI) applications have thus far been based on the use of medium resolution satellite imagery, such as Landsat TM. The high plot and stand level estimation errors of these applications have, however, restricted their use in forest management planning. One reason suggested for the high estimation errors has been the coarse spatial resolution of the imagery employed. Therefore, very high spatial resolution (VHR) imagery sources provide interesting data for stand-level inventory applications. However, digital interpretation of VHR imagery, such as aerial photographs, is more complicated than the use of traditional satellite imagery. Pixel-by-pixel analysis is not applicable to VHR imagery because a single pixel is small in relation to the object of interest, i.e. a forest stand, and therefore it does not adequately represent the spectral properties of a stand. Additionally in aerial photographs, the spectral properties of the objects are dependent on their location in the image. Therefore, MSFI applications based on aerial imagery must employ features that are less sensitive to their location in the image and that have been derived using the spatial neighborhood of each pixel, e.g. a square-shaped window of pixels. In this experiment several spectral and textural features were extracted from color-infrared aerial photographs and employed in estimation of forest attributes. The features were extracted from original, normalized difference vegetation index and channel ratio images. The correlations between the extracted image features and forest attributes measured from sample plots were examined. Additionally, the spectral and textural features were used for estimating the forest attributes of sample plots, applying the k nearest neighbor estimation method. The results show that several spectral and textural image features that are moderately or well correlated with the forest attributes. Furthermore, the accuracy of forest attribute estimation can be significantly improved by a careful selection of image features.     

基于分水岭变换与空间聚类的高分辨率遥感影像面向对象分类

面向对象方法已广泛应用于高分辨率遥感影像分类,提出一种结合改进分水岭变换与空间聚类的遥感影像面向对象分类新方法。首先,基于相位一致思想分析图像特征,由Gabor小波多尺度、多方向提取QuickBird全色影像的梯度信息;利用扩展最小变换与强制最小技术分别获取图像前景标识、重建相位一致梯度图像,利用改进后的分水岭变换获得分割对象。然后,提取各对象的多波段光谱特征,利用Gabor小波获取对象纹理矢量,并用独立成分分析方法进行特征选择,依次进行对象的光谱与纹理聚类。最后,通过分析对象间空间拓扑关系判断聚类后不确定对象的类别属性。实验结果表明该方法能取得较好结果,在一定程度上提高了影像分类的自动化水平。     

被动微波遥感数据超分辨率增强与混合像元分解研究综述

星载被动微波遥感数据以其全天候、穿透性以及不受云干扰等特点,在全球变化研究领域取得了广泛的应用,然而其较低的空间分辨率,限制了后期地物参数的反演精度。对国内外被动微波遥感数据空间分辨率提高方法进行介绍,重点介绍了基于图像处理技术的超分辨率增强和混合像元分解方法。通过对两类方法的介绍和评价,展望被动微波遥感数据混合像元分解方法的研究前景。被动微波遥感数据空间分辨率的有效提高,可以为更多的研究和应用领域服务。     

地理信息系统和遥感结合的现状及发展趋势

地理信息系统和遥感是支撑现代地理学的两大空间技术工具，近年来它们的结合越来越引起了人们的兴趣与研究。文章着重论述了地理信息系统和遥感结合的现状及其发展趋势，包括地理信息系统和遥感结合的内在原因、方式和存在的主要问题，并介绍了加拿大遥感中心在此方面的新的研完成果。     

条件生成对抗遥感图像时空融合

目的 卫星遥感技术在硬件方面的局限导致获取的遥感图像在时间与空间分辨率之间存在矛盾,而时空融合提供了一种高效、低成本的方式来融合具有时空互补性的两类遥感图像数据（典型代表是Landsat和MODIS （moderate-resolution imaging spectroradiometer）图像）,生成同时具有高时空分辨率的融合数据,解决该问题。方法 提出了一种基于条件生成对抗网络的时空融合方法,可高效处理实际应用中的大量遥感数据。与现有的学习模型相比,该模型具有以下优点：1）通过学习一个非线性映射关系来显式地关联MODIS图像和Landsat图像;2）自动学习有效的图像特征;3）将特征提取、非线性映射和图像重建统一到一个框架下进行优化。在训练阶段,使用条件生成对抗网络建立降采样Landsat和MODIS图像之间的非线性映射,然后在原始Landsat和降采样Landsat之间训练多尺度超分条件生成对抗网络。预测过程包含两层：每层均包括基于条件生成对抗网络的预测和融合模型。分别实现从MODIS到降采样Landsat数据之间的非线性映射以及降采样Landsat与原始Landsat之间的超分辨率首建。结果 在基准数据集CIA （coleam bally irrigation area）和LGC （lower Gwydir catchment）上的结果表明,条件生成对抗网络的方法在4种评测指标上均达到领先结果,例如在CIA数据集上,RMSE （root mean squared error）、SAM （spectral angle mapper）、SSIM （structural similarity）和ERGAS （erreur relative global adimensionnelle desynthese）分别平均提高了0.001、0.15、0.008和0.065;在LGC数据集上分别平均提高了0.001 2、0.7、0.018和0.008 9。明显优于现有基于稀疏表示的方法与基于卷积神经网络的方法。结论 本文提出的条件生成对抗融合模型,能够充分学习Landsat和MODIS图像之间复杂的非线性映射,产生更加准确的融合结果。     

遥感湖泊面积MTMF提取方法及空间尺度效应研究

主要研究遥感湖泊面积亚像元分解提取方法和空间尺度效应,为遥感湖泊面积提取、检验及基于此的局地气候变化分析提供科学的基础数据。在对TM遥感数据进行升尺度处理的基础上,采用混合调制匹配滤波(Mixture Tuned Matched Filtering,MTMF)进行亚像元分解,得到不同空间分辨率的湖泊面积。进而分析不同面积湖泊随遥感空间尺度的变化。结果表明:(1)当通过对高空间分辨率的遥感数据重采样获取多尺度遥感影像进行湖泊面积提取及湖泊空间尺度效应分析时,采用最近邻法比像元聚合重采样法更合理。(2)MTMF亚像元分解法可以用于基于水体光谱特征的遥感湖泊边界提取和面积计算,但边界提取过程中容易将湖泊与河流或其他非湖泊的水体混淆。(3)遥感湖泊面积的提取结果受所用遥感影像空间分辨率的影响较大,影像的空间分辨率越低,湖泊面积提取的偏差越大,尤其对面积较小的湖泊。     

遥感技术在我国滑坡研究中的应用综述

中国滑坡遥感研究与应用已有30多年的发展历史,作为滑坡调查的主要手段在大型工程建设中的滑坡灾害调查及危险性评价中发挥了重要作用。从4方面阐述了遥感技术在我国滑坡研究中的应用：① 区域滑坡灾害遥感调查;② 大型单体滑坡遥感调查;③ 滑坡灾害遥感监测;④ 遥感应用于滑坡风险评估。随着遥感技术理论的逐步完善和遥感图像空间分辨率、时间分辨率与波谱分辨率的不断提高,遥感技术已成为滑坡灾害调查、动态监测与预警、灾情实时调查与损失评估等工作中不可缺少的重要手段之一。     

Fine spatial resolution satellite sensors for the next decade

Following the end of the Cold War governmental restrictions on the commercial availability of fine spatial resolution satellite sensor imagery have been relaxed world-wide. This, combined with marked reductions in the costs of developing, launching and operating satellites, has led to considerable research activity in this field by a number of private remote sensing organisations. Within the next few years, imagery with a spatial resolution as fine as 1 m in panchromatic mode and 4 m in multispectral mode will be available widely. This Letter presents a review of fine spatial resolution satellite sensors in operation or planned for operation within the next decade. Details of both commercial and governmental systems are provided. The emphasis is on commercially available data and so data collected for defence applications only are not included. A variety of both instrument and data specifications are highlighted, including spatial and spectral capabilities, and characteristics of viewing geometry, satellite orbit, data collection and supply. Typically, these systems are characterized not only by their fine spatial resolution, but also by high geometric precision, short revisit intervals and rapid data supply.     

遥感图像时空融合综述

高时空分辨率的遥感图像大数据在遥感领域发挥着重要作用.然而,由于技术上和预算上的限制等原因,目前单一的卫星传感器无法获取同时具有高空间分辨率和高时间分辨率的遥感影像.因此遥感图像时空融合技术被认为是解决时间分辨率和空间分辨率折衷问题的有效途径之一.随着深度学习在各领域的广泛应用,深度学习技术已经被证实是解决图像问题非常...     

联合快舟一号影像纹理信息的城市土地覆盖分类

仅依靠光谱信息无法满足高分辨率遥感分类的应用需求,辅之以纹理特征信息进行分类,可提高影像分类精度。利用KZ\|1卫星影像和Landsat\|8卫星影像数据,基于面向对象的影像分割法和灰度共生矩阵纹理分析法对新疆石河子市局部城区进行了地表覆盖分类实验,将不同空间分辨率的全色影像纹理信息、光谱信息构成多种影像特征组合进行分类比较研究,以选择最佳的分类特征集。结果表明:KZ-1影像能为城市区域的土地覆盖分类提供丰富的纹理信息,面向对象的影像分割可较好地利用高分辨率数据的几何结构信息实现优化的影像分割,从而提高多光谱影像的分类精度,总体分类精度为90.06%,Kappa系数为87.93%,比单纯利用光谱信息分类的总体精度提高了8.02%,Kappa系数提高了9.65%,表明KZ\|1数据可为光谱分类提供丰富的纹理信息,从而提高城市区域的土地覆盖分类精度。     

基于多源遥感数据的区域景观格局尺度效应

无论在景观生态学还是在遥感领域,尺度问题都是非常重要的问题,目前已有的研究主要考虑景观的粒度效应,很少涉及遥感影像空间分辨率对景观格局的影响。在遥感和地理信息系统软件的支持下,利用多源遥感影像,结合研究区的特点进行了景观分类,并从不同遥感数据的空间分辨率角度进行空间粒度放大试验,探讨景观格局的尺度效应。研究结果表明：基于不同空间分辨率遥感影像的土地利用空间数据具有尺度效应,其所反映的区域土地利用景观格局在宏观上是一致的,但类型的边界、形状和数量均产生较大的差异;景观格局指数能反映不同遥感影像所记录的地表信息,从不同遥感数据源、不同空间分辨率的角度定量化判断尺度放大过程中区域土地利用景观特征信息的尺度效应。     

A new image classification approach for mapping coral density in State of Kuwait using high spatial resolution satellite images

Remote sensing technology can be a valuable tool for mapping coral reef ecosystems. However, the resolution capabilities of remote sensors, the diversity and complexity of coral reef ecosystems, and the low reflectivity of marine environments increase the difficulties in identifying and classifying their features. This research study explores the capability of high spatial resolution (WorldView-2 (WV-2) and Pleiades-1B) and low spatial resolution (Land Remote-Sensing Satellite (Landsat 8)) multispectral (MS) satellite sensors in quantitatively mapping coral density. The Kubbar coral reef ecosystem, located in Kuwait’s southern waters, was selected as the research site. The MS imagery of WV-2, Pleiades-1B and Landsat 8 were, after geometric and radiometric assessment and corrections, subjected to new image classification approach using a Multiple Linear Regression (MLR) analysis. The new approach of MLR coral density analysis used the dependent variable of coral density percentage from ground truth and independent variables of spectral reflectance from selected imagery, depth (as estimated from a surface derived from bathymetric charts) and distance to land or reef unit centre. Accuracy assessment using independent ground truth was performed for the selected approach and satellite sensors to determine the quality of the information derived from image classification processes. The results showed that coral density maps developed using the MLR coral density model proved to have some level of reliability (radiometrically corrected WV-2 image (the coefficient determination denoted as R-squared (R²) = 0.5, Root-Mean-Square Error (RMSE) = 10) and radiometrically corrected Pleiades-1B image (R² = 0.8, RMSE = 10)). This study suggested using high spectral resolution data and including additional factors (variables) (e.g. water turbidity, temperature and salinity) could contribute to improving the accuracy of coral density maps produced by application of the MLR model; however, all of these would add cost and effort to the mapping process. The outcomes of this research study provide coral reef ecosystem researchers, managers, and decision makers a tool to determine and map coral reef density in more detail than in the past. It will help quantify coral density at particular points in time leading to estimates of change, and allow coral reef ecologists to identify the current coral reef habitat health status, distribution and extent.     

应用Landsat数据和SEBAL模型反演区域蒸散发及其参数估算

随着遥感技术的进步,人们发展了多种通过遥感计算区域蒸散发的方法。SEBAL模型是通过遥感反演地面蒸散的典型方法,它以陆面能量平衡为基础,物理意义明确,只利用遥感影像和少量的气象数据（风速、气温）就能反演蒸散量。Landsat数据的波谱信息丰富、空间分辨率高,数据源稳定,是通过遥感技术反演蒸散发的理想数据源。如果能合理地估算SEBAL模型中的基本参数,将会获得较高精度的反演结果,能够满足在水文、生态、林业等研究或应用中的需要,对区域水资源的管理与利用具有重要意义。从SEBAL模型的基本原理出发,分析了利用SEBAL模型采用Landsat的TM/ETM+数据反演区域蒸散发的基本过程,针对TM/ETM+数据特点对模型所需要的基本参数进行估算求解,为SEBAL模型在蒸散量反演中的广泛应用提供了一定的指导。     

Quantifying high‐resolution impervious surfaces using spectral mixture analysis

Impervious surface distribution and its temporal changes are considered key urbanization indicators and are utilized for analysing urban growth and influences of urbanization on natural environments. Recently, urban impervious surface information was extracted from medium/coarse resolution remote sensing imagery (e.g. Landsat ETM+ and AVHRR) through spectral analytical methods (e.g. spectral mixture analysis (SMA), regression tree, etc.). Few studies, however, have attempted to generate impervious surface information from high resolution remotely sensed imagery (e.g. IKONOS and Quickbird). High resolution images provide detailed information about urban features and are, therefore, more valuable for urban analysis. The improved spatial resolution, however, also brings new challenges when existing spectral analytical methods are applied. In particular, a higher spatial resolution leads to reduced boundary effects and increased within‐class variability. Taking Grafton, Wisconsin, USA as a study site, this paper analyses the spectral characteristics of IKONOS imagery and explores the applicability of SMA for impervious surface estimation. Results suggest that with improved spatial resolution, IKONOS imagery contains 40–50% of mixed urban pixels for the study area, and the within‐class variability is a severe problem for spectral analysis. To address this problem, this paper proposes two approaches, interior end‐member set selection and spectral normalization, for SMA. Analysis of results indicates that these approaches can reasonably reduce the problems associated with boundary effects and within‐class variability, therefore generating better impervious surface estimates.     

结合像元形状特征分割的高分辨率影像面向对象分类

针对高分辨率遥感影像空间分辨率高,结构形状、纹理、细节信息丰富等特点,提出一种新的融合特征的面向对象影像分类方法来提取城市空间信息。基本过程包含以下4个方面:①提取影像的几何纹理等结构;②融合几何与纹理特征的面向对象影像分割;③提取对象的形状、纹理和光谱特征,并优选最佳特征子集;④最后基于支持向量机(SVM)完成面向对象的影像分类。通过对福州IKONOS影像数据实验,结果表明融入影像特征后的分割效果明显优于原始影像的分割结果,而信息最大化(mRMR)的特征选择能够快速地获得较好的特征子集。通过与eCognition最邻近分类方法比较,表明本文方法的分类总体精度大约提高了6%,效果显著。