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991.
Hydrocarbon micro and macro seeps alter chemical and mineral composition of the Earth’s surface, providing prospects for detection with remote sensing tools. There have been several studies focusing on mapping these anomalies by utilizing ever evolving multispectral and hyperspectral imaging instruments, which has proven their capacity for mapping both hydrocarbons and hydrocarbon-induced alterations so far. These studies broadly comprise of methods like calculating band ratios, spectral angle mapping, spectral feature fitting, and principal component analysis as detection techniques. However, there is a lack of concentration on advanced signature based detection algorithms and unmixing methods for mapping surface manifestations of hydrocarbon microseeps. Signature based detection algorithms utilize target spectra to correlate with each pixel’s spectrum in order to allocate possible target locations. Unmixing methods, on the other hand, require no input spectra beforehand, aiming to resolve each pixel’s spectral constituents and their corresponding abundance fractions. In this paper, the potential of all these methods in mapping microseepage related anomalies are evaluated by implementing and comparing them for Gemrik Anticline, one of the prospective hydrocarbon exploration fields in Turkey. Hence, it provides a complete knowledge on determination surface manifestations of hydrocarbon microseeps with the help of well known supervised target detection algorithms and hyperspectral unmixing algorithms. The study area is located in the Southeastern Anatolia, between the cities of Adıyaman and Şanlıurfa. The spectral signatures were collected with Analytical Spectral Devices Inc. (ASD) spectrometer during the field studies conducted by Avcıoğlu (2010), to be utilized as an input to the signature based detection algorithms as well as a reference to select the related abundance map among the outputs of unmixing methods. Advanced Space Borne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction before running the algorithms, is selected for the applications. Among the applied algorithms, Simplex Identification via Split Augmented Lagrangian (SISAL) is selected as a base of comparison, as it possess minimum calculated error metrics in the experiments. Another unmixing method, the Minimum Volume Simplex Algorithm (MVSA), and signature-based techniques, Desired Target Detection and Classification Algorithm (DTDCA) & Spectral Matched Filter (SMF) follow the success of the SISAL, respectively. The Crosta technique, which is performed as a conventional approach for experimental comparisons, has also shown its capability, succeeding these algorithms. The study provides an overall assessment for methodologies to be used for hydrocarbon microseepage mapping, which also serves guidance for further exploration studies in the region. The potential of ASTER data for hydrocarbon-induced alterations is also emphasized as a cost effective tool for the future applications. 相似文献
992.
无人车平台激光点云中线特征提取 总被引:1,自引:0,他引:1
随着无人平台的应用越来越广泛,由激光点云提取线特征构建高精度特征地图已成为研究的重点。本文基于深度图像中的二维线特征,提出了一种新的几何模型对其进行优化,得到准确的三维线特征,并使用贝叶斯滤波对多帧结果进行融合,提高了三维再线特征的精度和准确率。 相似文献
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圆形标志已广泛应用于近景摄影测量,但由于影像平面与目标平面不能完全平行,所以圆形标志的数字影像一般为椭圆。如果圆形标志点较大,以至于圆形标志中心与其椭圆构像中心的偏差不可忽略。故本文从摄影测量共线条件方程式出发,采用一种基于最小二乘的椭圆拟合算法,推导出偏差计算公式,本文的真正目的在于分析偏差分布规律及偏差间的关系,尽可能降低偏差的影响。 相似文献
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找矿效果取决于找矿靶区预测的准确程度,传统的“综合信息成矿预测”(定性研究)已无法深入挖掘现有地质信息的潜在价值。本文应用大数据思想、方法,对甘肃省祁连山—龙首山地区1∶20万区域化探数据做分幅平差,消除了原始数据的系统误差。应用回归分析建立信息修复模型,增强了化探信息与区域Cu矿的相关关系。通过判别分析算法,构建了区域“化探信息Cu找矿靶区定量优选系列模型”,对研究区Cu找矿靶区做出定量预测。经统计,一、二级预测靶区中包含已知铜矿的比率高于22.0%,其面积仅占总研究区的1.72%。大数据找矿靶区定量预测在大幅提高预测精度的同时,很大程度地缩小了预测找矿靶区的面积。在对系列模型预测效果做出定量评价的同时,通过所建系列模型组合元素的特征分析,该研究也为进一步研究区域矿床成因和控矿条件提供了定量依据。 相似文献