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野外波谱测量的影响因素研究 总被引：7，自引：0，他引：7
地物的光谱特征研究是现代遥感技术的重要组成部分,野外地物波谱测量对航天航空传感器定标、遥感数据解译、遥感应用潜力研究都具有重要意义。在野外采集准确的地物波谱需要熟悉光照条件、大气特性和稳定性、仪器视场角、观测和光线照射的几何角度、仪器扫描速度以及目标的时空变化等各种因素对测量结果的影响。该文从实践角度探讨了地物波谱测量的原理以及各种外界因素对地物光谱测量的影响。 相似文献
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高光谱遥感数据具有波段数目多、波段宽度窄、数据量庞大、波段间相关性高等特点,在一定程度上为图像的进一步处理和信息提取带来困难.为解决这一问题,在分析已有降维方法的基础上,提出了基于地物诊断性波谱吸收特征的高光谱遥感图像降维方法,将地物的诊断性吸收波谱特征区间作为一个独立的子空间进行处理,尽可能保留地物独有的吸收特征;在此基础上,进行子空间的特征提取和特征选择.为验证该方法的优越性,将其与传统的基于波谱区间的子空间划分方法进行分类对比,研究表明:基于该文方法降维后的图像分类精度更高,丰富了现有降维方法理论,具有一定的实用和推广价值. 相似文献
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腾格里沙漠典型植物含水率与地物光谱的关系分析 总被引：2，自引：0，他引：2
地物光谱特征不仅是遥感机理研究的重要内容,亦是遥感应用分析的重要依据。用ASD手持式光谱仪测定了腾格里沙漠10种典型植物的冠层光谱,对测得光谱数据进行包络线去除和一阶微分处理,并在实验室采用烘干法得到植物的含水率,运用相关系数法分析植物含水率与经包络线去除的光谱数据之间的关系,同时分析了不同含水率植物的红边特征。结果表明：研究区植物最大含水率为88.19%,最小含水率为37.34%;含水率与包络线去除的光谱数据在可见光（561~718 nm）和近红外（861 nm,894 nm）波段均存在极显著相关性,并建立了两者之间的回归模型,说明可见光近红外波段可以反映沙漠植物的水分状况;不同含水率植物的红边参数有所差异。 相似文献
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随着现代科技的发展及经济建设的需要,航空遥感技术应用的范围越来越广泛。我所近几年对航空遥感技术,初步进行了多方面的试验研究。现就航空遥感技术在地理学中的应用问题,结合我们在工作中的经验谈一些体会。 一、航空遥感的特点 遥感技术之所以能够探测到各种地面目标,是以地面目标本身具有不同的电磁波辐射或反射特性为根据的。不同地物波谱特性的差异便成为遥感的重要理论基础。根据各种物体不同波长的反射率差异而产生的在彩红外或多光谱相片上色调的变化,便能准确地将各种不同的地物区分开来。即可以通过图像对各种地物进行研究分析,从而得到大量地面实况信息。 我所自1979年建立遥感室以来,已进行过多方面的试验研究。从工作实践中,我们认为航空遥感技术在应用方面具有以下特点: 1.信息量大。航空象片记录了地表景观的全部 相似文献
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应用MODIS卫星数据提取植被-温度-水分综合指数的研究 总被引：17，自引：1，他引：16
中分辨率成像光谱仪(MODIS)覆盖可见光、近红外和热红外的36个波段,其波谱分辨率高、信息量丰富.通过分析MODIS数据的波谱特性以及植被和土壤的反射波谱特征,选用可见光波段(0.66 μm)、近红外波段(0.86 μm、1.24 μm)提取修正的土壤调整植被指数(MSAVI)和归一化植被水分指数(NDWI);用两个热红外波段(8.6 μm、11 μm)反演植被冠层温度,并通过分析三者之间的耦合特征来提取反映植被水分状况的综合指标(VTWSI).用实测的植被水分数据和模拟的叶片等效水分厚度数据验证所提取的VTWSI值,拟合结果表明呈显著正相关,说明所提取的VTWSI可有效反映植被的水分状况.该项研究探讨了一种直接从卫星遥感数据提取植被水分指标的新方法,为研究干旱、半干旱地区的区域缺水提供简便途径. 相似文献
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抛光花岗岩的二向反射比与偏振度的波谱关系 总被引：1，自引：0，他引：1
传统遥感定义双向反射率分布函数(BRDF)的概念,用以表达目标物空间反射的多角度分布特征,但其测量多偏重于单波长的微波及激光。偏振伴随多角度探测而生,蕴涵了目标物的自身特性。该文就城市常见地物——抛光的花岗岩,分别测量了其在350～2 500nm范围内的二向反射比和偏振度的波谱,分析并比较了两者波谱随探测角度的变化规律,并从定义上分析了两者的波谱关系,发现其随探测天顶角及方位角的变化规律一致,而且二向反射比和偏振度的波谱在一定程度上呈反比关系。 相似文献
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基于三维激光点云的复杂道路场景杆状交通设施语义分类 总被引：1，自引：0，他引：1
文章提出一种完整的全自动化处理框架,基于三维激光点云数据对高速公路和城市道路场景的杆状目标进行了检测和分类,主要包括3个步骤：数据预处理、杆状目标检测和分类。其中,在数据预处理阶段,采用基于布料模拟滤波算法自动分离地面点和非地面点,然后基于欧氏距离聚类方法对非地面点进行快速聚类,以及采用迭代图割算法进一步分割目标对象;在目标检测阶段,集成先验信息、形状信息和位置导向搭建滤波器,对杆状目标进行检测;在对象分类过程中基于多属性特征,利用随机森林分类器对目标的特征进行计算和分类。并使用3个道路场景数据集进行测试,结果显示,3个数据集的整体MCC系数为95.6%,分类准确率为96.1%。这说明文章所构建方法具有较高性能。另外,该方法还可以鲁棒地检测杆状目标的重叠区域,较为适应复杂程度不同的道路场景。 相似文献
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Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network 总被引：1，自引：0，他引：1
Ruibin Zhao Jidong Wang 《International journal of geographical information science》2018,32(5):960-979
Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods. 相似文献
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Massimiliano Favalli Alessandro Fornaciai Maria Teresa Pareschi 《Geomorphology》2009,111(3-4):123-135
DEMs derived from LIDAR data are nowadays largely used for quantitative analyses and modelling in geology and geomorphology. High-quality DEMs are required for the accurate morphometric and volumetric measurement of land features. We propose a rigorous automatic algorithm for correcting systematic errors in LIDAR data in order to assess sub-metric variations in surface morphology over wide areas, such as those associated with landslide, slump, and volcanic deposits. Our procedure does not require a priori knowledge of the surface, such as the presence of known ground control points. Systematic errors are detected on the basis of distortions in the areas of overlap among different strips. Discrepancies between overlapping strips are assessed at a number of chosen computational tie points. At each tie point a local surface is constructed for each strip containing the point. Displacements between different strips are then calculated at each tie point, and minimization of these discrepancies allows the identification of major systematic errors. These errors are identified as a function of the variables that describe the data acquisition system. Significant errors mainly caused by a non-constant misestimation of the roll angle are highlighted and corrected. Comparison of DEMs constructed using first uncorrected and then corrected LIDAR data from different Mt. Etna surveys shows a meaningful improvement in quality: most of the systematic errors are removed and the accuracy of morphometric and volumetric measurements of volcanic features increases. These corrections are particularly important for the following studies of Mt. Etna: calculation of lava flow volume; calculation of erosion and deposition volume of pyroclastic cones; mapping of areas newly covered by volcanic ash; and morphological evolution of a portion of an active lava field over a short time span. 相似文献
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城市道路数据的完整性和实时性是保障位置服务和规划导航路径的关键支撑。该文提出一种基于共享单车轨迹数据的新增自行车骑行道路自动检测和更新方法:首先,结合缓冲区方法和轨迹—路网几何特征检测增量轨迹;其次,基于分段—聚类—聚合策略提取更新路段,利用多特征融合密度聚类算法与最小外包矩形骨架线法提取增量道路中心线;最后,基于拓扑规则完成道路更新。以广州市共享单车轨迹为例,将该方法与传统栅格细化法进行实验对比,结果表明:该方法能有效更新道路网络,且在2 m和5 m精细尺度范围内提取的新增道路覆盖精度提升14%左右;在7 m尺度下精度达90%以上,在10 m尺度下精度达96%以上。 相似文献
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Christopher D. Lloyd Peter M. Atkinson 《International journal of geographical information science》2013,27(5):535-563
This paper focuses on two common problems encountered when using Light Detection And Ranging (LiDAR) data to derive digital elevation models (DEMs). Firstly, LiDAR measurements are obtained in an irregular configuration and on a point, rather than a pixel, basis. There is usually a need to interpolate from these point data to a regular grid so it is necessary to identify the approaches that make best use of the sample data to derive the most accurate DEM possible. Secondly, raw LiDAR data contain information on above‐surface features such as vegetation and buildings. It is often the desire to (digitally) remove these features and predict the surface elevations beneath them, thereby obtaining a DEM that does not contain any above‐surface features. This paper explores the use of geostatistical approaches for prediction in this situation. The approaches used are inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT). It is concluded that, for the case studies presented, OK offers greater accuracy of prediction than IDW while KT demonstrates benefits over OK. The absolute differences are not large, but to make the most of the high quality LiDAR data KT seems the most appropriate technique in this case. 相似文献
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为了在保持对目标检测和分类分析所需信息的同时,降低高光谱影像的维度,提出了一种混合优化策略的特征选择方法。该方法将遗传算法和二进制粒子群优化算法融合成一种新的混合优化策略（GANBPSO）,自动选择最优波段组合,同时优化分类器支持向量机（RBF-SVM）的参数,以提高分类器的分类性能。为了说明所提出方法的有效性,采用了在高光谱分类中广泛使用的Indian Pines（AVIRIS 92AV3C）数据集进行测试。结果表明所提出方法（GANBPSO-SVM）能够自动选择包含最多信息的特征子集以保证分类精度,而不需要预先设置所需要的特征子集数量,本方法与传统方法相比具有更好的分类效果。 相似文献
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It is a very complicated problem to estimate evapotranspiration (ET) over a large area of land surface. In this paper, the evapotranspiration estimation models for dense vegetation and bare soil are presented, based on the information of parameters like vegetation cover-degree and surface albedo. Combined with vegetation cover-degree data, a model for regional evapotranspiration estimation over the heterogeneous landscape is derived. Through a case study using remote sensing data over Northwest China, the accuracy of the model for regional evapotranspiration estimation is checked. The result shows that the accuracy of the model is satisfactory. The features of evapotranspiration over Northwest China are also discussed with the application of the model. 相似文献
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1 IntroductionThe method for estimating evapotranspiration (ET) was first given by Dalton in 1802, and a number of models for ET estimation have been presented since then. Those models, from the experiential and semi-experiential models[1,2] and physical models[3,4] to the models in terms of the mechanism for energy and water fluxes in soil-vegetation-atmosphere transfer system such as SiB, have improved the precision of ET estimation. It is, however, difficult to calculate regional ET by … 相似文献