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自动驾驶技术已成为未来智能交通的发展方向之一,高精度地图为L3级及以上自动驾驶实现高精度定位和路径规划提供先验信息,是自动驾驶车辆传感器在遮挡或观测距离受限情况下的重要补充。道路标线的位置和语义信息,比如实线和虚线的绝对位置是高精度地图的基本组成部分。本文从车载激光点云中提取扫描线,根据道路边缘位置几何形态的突变从扫描线中提取道路路面,在此基础上首先利用反距离加权插值的方法把路面点云图像以一定的分辨率转换为栅格图像,其次利用基于积分图的自适应阈值分割方法把栅格图像转化为二值图像,然后利用欧氏聚类的方法从二值图像中提取标线点云,并利用特征属性筛选的方法对提取的标线点云进行语义识别,最后建立交通标线和交通规则之间的语义关联。 相似文献
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Álvaro Gómez-Gutiérrez Trent Biggs Napoleon Gudino-Elizondo Paz Errea Esteban Alonso-González Estela Nadal Romero José Juan de Sanjosé Blasco 《地球表面变化过程与地形》2020,45(11):2524-2539
Image network geometry, including the number and orientation of images, impacts the error, coverage, and processing time of 3D terrain mapping performed using structure-from-motion and multiview-stereo (SfM-MVS). Few studies have quantified trade-offs in error and processing time or ways to optimize image acquisition in diverse topographic conditions. Here, we determine suitable camera locations for image acquisition by minimizing the occlusion produced by topography. Viewshed analysis is used to select the suitable images, which requires a preliminary digital elevation model (DEM), potential camera locations, and sensor parameters. One aerial and two ground-based image collections were used to analyse differences between SfM-MVS models produced using: (1) all available images (ALL); (2) images selected using conventional methods (CON); and (3) images selected using the viewshed analysis (VIEW). The resulting models were compared with benchmark point clouds acquired by a terrestrial laser scanner (TLS) and TLS-derived DEMs. The VIEW datasets produced denser point clouds (28–32% more points) and DEMs with up to 66% reduction in error compared with CON datasets due to reduction of gaps in the DEM. VIEW datasets reduced processing time by 37–76% compared with ALL, with no reduction in coverage or increase in error. DEMs produced with ALL and VIEW datasets had similar slope and roughness, while slight differences that may be locally important were observed for the CON dataset. The new method helps optimize SfM-MVS image collection strategies that significantly reduce the number of images required with minimal loss in coverage or accuracy over complex surfaces. © 2020 John Wiley & Sons, Ltd. 相似文献
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Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation, influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing close aerial sensing data to evaluate how high-resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over 7 months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April, resulting in nine time steps of data acquisition. Structure from motion photogrammetry was used to derive high-resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation-induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height, applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research. © 2020 John Wiley & Sons, Ltd. 相似文献
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应用改进的图像信息方法, 结合DEMETER电磁卫星的电子温度数据, 对2008年汶川MS8.0地震进行了长时程的回溯性预测研究。 与以往的地震电离层演化图像提取异常的研究相比, 将其回溯的时程延长至震前近11个月, 并向震后延长48 d。 在长期连续的异常扰动演化图中, 出现了两个明显的异常时段, 且在震前2个月短期内异常扰动明显减弱并趋于背景值。 两个时段出现的异常扰动分布位置及其形态以及持续时间皆不重复, 这体现了前兆异常的不易确定, 难以定性及定量识别的特点。 电离层异常扰动特征的长时程研究, 有评估其预测参数和模型可靠性的实际作用, 也为应用中国卫星观测数据服务于地震预报等研究积累经验, 具有借鉴意义。 相似文献
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长期连续完整的历史气温资料是震前气温异常判别研究的重要数据基础。本文考虑了参考站与缺测站之间的距离,建立改进的线性回归模型。利用该模型插补缺测和错误的气温整点值数据,在一定程度上解决了长期连续观测数据缺测的情况。通过对收集的唐山观测站气温整点值数据进行插补,并应用插补完整的数据分析研究了2012年5月28日唐山4.8级地震前兆异常。结果表明:①插补值与其前后观测值衔接吻合,插补后完整连续数据符合夏高冬低的年变规律;②插补误差在±0.5℃范围内的比例为60.2%,在±0.8℃范围内的比例为80.3%,其误差绝对值大于1.0℃的比例为9.6%,平均绝对误差为0.84℃,插补值与观测值的相关系数大部分在0.9以上;③从3月27日起出现增温异常,特别是震前2天增温幅度约8℃。 相似文献
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通过分析墨江5.9级地震前预测意见和预测资料,认为云南地区M≥5.5地震平静异常突出,M≥5.0地震连续间隔时间达200天以上,通海2次5.0级地震震级偏小是震前地震活动异常的显著特征;通海地震的指示意义、前兆异常M≥5.7地震中期和M≥5.0地震短临综合预测指标是判断地震短期可能性的主要依据;1900年以来,通海50km范围内M≥5.0地震后云南地区发生M≥5.0地震优势对应关系和GNSS表征的区域面应变变化特征为地点判定提供了线索。墨江5.9级地震的预测经验及资料的论证丰富了对地震孕育复杂性的认识,为震例积累了新的资料。 相似文献