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Yanyan Liu Yidong Lou Shirong Ye Rui Zhang Weiwei Song Xing Zhang Qingquan Li 《GPS Solutions》2017,21(4):1647-1659
Although integer ambiguity resolution (IAR) can improve positioning accuracy considerably and shorten the convergence time of precise point positioning (PPP), it requires an initialization time of over 30 min. With the full operation of GLONASS globally and BDS in the Asia–Pacific region, it is necessary to assess the PPP–IAR performance by simultaneous fixing of GPS, GLONASS, and BDS ambiguities. This study proposed a GPS + GLONASS + BDS combined PPP–IAR strategy and processed PPP–IAR kinematically and statically using one week of data collected at 20 static stations. The undifferenced wide- and narrow-lane fractional cycle biases for GPS, GLONASS, and BDS were estimated using a regional network, and undifferenced PPP ambiguity resolution was performed to assess the contribution of multi-GNSSs. Generally, over 99% of a posteriori residuals of wide-lane ambiguities were within ±0.25 cycles for both GPS and BDS, while the value was 91.5% for GLONASS. Over 96% of narrow-lane residuals were within ±0.15 cycles for GPS, GLONASS, and BDS. For kinematic PPP with a 10-min observation time, only 16.2% of all cases could be fixed with GPS alone. However, adding GLONASS improved the percentage considerably to 75.9%, and it reached 90.0% when using GPS + GLONASS + BDS. Not all epochs could be fixed with a correct set of ambiguities; therefore, we defined the ratio of the number of epochs with correctly fixed ambiguities to the number of all fixed epochs as the correct fixing rate (CFR). Because partial ambiguity fixing was used, when more than five ambiguities were fixed correctly, we considered the epoch correctly fixed. For the small ratio criteria of 2.0, the CFR improved considerably from 51.7% for GPS alone, to 98.3% when using GPS + GLONASS + BDS combined solutions. 相似文献
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Selective omission is necessary for road network generalisation. This study investigates the use of supervised learning approaches for selective omission in a road network. To be specific, at first, the properties to measure the importance of a road in the network are viewed as input attributes, and the decision of such a road is retained or not at a specific scale is viewed as an output class; then, a number of samples with known input and output are used to train a classifier; finally, this classifier can be used to determine whether other roads to be retained or not. In this study, a total of nine supervised learning approaches, i.e., ID3, C4·5, CRT, Random Tree, support vector machine (SVM), naive Bayes (NB), K-nearest neighbour (KNN), multilayer perception (MP) and binary logistic regression (BLR), are applied to three road networks for selective omission. The performances of these approaches are evaluated by both quantitative assessment and visual inception. Results show that: (1) in most cases, these approaches are effective and their classification accuracy is between 70% and 90%; (2) most of these approaches have similar performances, and they do not have any statistically significant difference; (3) but sometimes, ID3 and BLR performs significantly better than NB and SVM; NB and KNN perform significantly worse than MP, SVM and BLR. 相似文献
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基于GIS的北京市生态环境质量监测与分析 总被引:1,自引:0,他引:1
以2006年TM、SPOT 4及QuickBird遥感影像为信息源,在地理信息系统与遥感技术的支持下,结合野外调查及其它辅助数据,运用生态环境状况评价技术规范,对北京市2006年的生态环境质量进行评价,并对2000年与2006年北京市生态环境变化进行动态分析.结果表明: 2006年北京市生态环境质量评价指数为59.707,处于良好等级;2000~2006年间,北京全市生态环境状况稳定保持在"良"的状态,总体变化不大,但北京市耕地面积减少了339.09 km2,城镇建设用地增加了285.24 km2,水域面积减少了118.98 km2,其主要原因是降水量的减少、城市化扩张以及人为因素影响. 相似文献
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CBERS-02B星HR与多光谱影像融合及评价 总被引:4,自引:0,他引:4
CBERS-02B星搭载了高分辨率全色相机HR和多光谱传感器CCD,HR影像可以与CCD影像融合,优势互补形成新的影像,既保持HR的高空间分辨率又保持CCD的光谱分辨率,HR影像同样可以与其它传感器影像融合形成新的影像。本文使用6种不同的融合方法融合HR与多光谱CCD以及SPOT5多光谱影像,并对融合结果进行了定性和定量评价,得到了HR与SPOT5多光谱影像融合较好的方法,表明了HR与其它传感器影像融合的潜力,同时也对HR和SPOT5多光谱融合影像及与CCD融合影像进行了初步的对比分析。 相似文献
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In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index zl and △x based on rough set theory of 5 sample data and also analyzes its effect on sample quality. 相似文献