排序方式： 共有14条查询结果，搜索用时 15 毫秒
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The Potential of Virtual Reality Technology for Analysis of Remotely Sensed Data: A Lidar Case Study
Timothy Warner M. Duane Nellis Tomas Brandtberg James B. McGraw Joseph V. Gardner 《国际地球制图》2013,28(1):25-32
Abstract Although the GIS community has been quick to exploit the advantages of virtual reality (VR) for display and analysis of spatial data, VR does not appear to have been exploited widely for remote sensing data analysis. A case study of high resolution lidar data acquired over a deciduous forest near Morgantown, WV was used to investigate the potential and limitations of current VR software for remote sensing analysis. The functionality within a standard remote sensing software package was found to provide a good overview of interpolated, smoothed lidar data, but was less useful for gridded data that had not been interpolated. With gridded data, it was possible to drape orthophotographs or other images over the lidar data, providing a useful method for investigating relationships between lidar and other data. Alternatively, using a commercial VR package, it was possible to view the original lidar point data, and thus visualize the multiple returns from within the canopy of each tree. The point data were preferable for identification of surfaces within the data cloud, especially the ground surface. For a fully integrated remote sensing VR package, functionality will be needed to link point and interpolated coverages, and also to enhance the interactive selection of data for further statistical analysis. 相似文献
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目前大多数面向像元、面向对象遥感影像分类对比研究算法、软件、样本均不同,引入多方面系统误差导致结果一定程度上不严谨。为更准确比较2种分类方法,本文采用面向像元、面向对象2种分类方式,在同软件平台、同分类器、同训练样本、同验证样本,即“四同”条件下对2018年4月17日高分一号周口城区融合影像进行分类对比研究,并完成主、客观评价精度评价。结果表明：① “四同”条件下2种分类方式、CART（Classification and Regression Tree）、SVM（Support Vector Machine）、RF（Random Forests）3种机器学习算法均能识别周口城区主要地物类型,而面向对象的分类效果明显优于面向像元分类,与前人研究结论一致。其中面向像元分类效果最好的是RF算法,总体分类精度为78.02%,Kappa系数为0.72;面向对象分类效果最好的是RF算法,总体分类精度为93.40%,Kappa系数为0.92;② 尽管由于光谱特征相似、分布交叉,单类别建筑用地、交通用地用户精度与生产者精度较低,但面向对象分类较面向像元分类效果明显提升,以RF分类为例,建筑用地生产者精度由56.18%提高至92.13%,用户精度由69.44%提高至87.23%;交通用地生产者精度由72.15%提高至89.87%,用户精度由72.15%提高至92.20%;③ 与前人研究成果比较,本文在“四同”条件下实现了更科学、更严谨的面向像元、面向对象遥感分类方法对比,对后续高分辨率遥感影像分类具有一定参考意义。 相似文献
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《International Journal of Digital Earth》2013,6(5):504-520
Abstract Because the removal of topographic effects is one the most important pre-processing steps when extracting information from satellite images in digital Earth applications, the problem of differential terrain illumination on satellite imagery has been investigated for at least 20 years. As there is no superior topographic correction method applicable to all areas and all images, a comparison of topographic normalization methods in different regions and images is necessary. In this study, common topographic correction methods were applied on an ALOS AVNIR-2 image of a rugged forest area, and the results were evaluated through different criteria. The results show that the simple correction methods [Cosine, Sun-Canopy-sensor (SCS), and Minnaert correction] are inefficient in exceptionally rough forests. Among the improved correction methods (SCS+C, modified Minnaert, and pixel-based Minnaert), the best result was achieved using a pixel-based Minnaert approach in which a separate correction factor in various slope angles is used. Thus, this method should be considered for topographic correction, especially in forests with severe topography. 相似文献
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