复杂城区车载激光扫描点云分层路段定位与数据纠正实验研究

高精度的车载点云数据是实现各种城市地物要素提取的前提,但在复杂城市环境下车载作业时信号遮挡、衰减和多径效应频繁发生,进而造成点云数据精度严重降低,如何对复杂环境下的点云数据进行分析和纠正就显得至关重要.现有的质量分析方法多从测距误差、仪器安装误差和数据处理误差方面出发,虽然可以在一定程度上改化数据,但在如何快速定位出分...     

高精度重力方法在高青地区探测古潜山构造中的应用

本文根据作者几年来把高精度重力勘探方法应用在胜利油田进行古潜山构造探测的生产实践经验,总结了在油气勘探中高精度重力方法应用的新进展,阐述了它在勘探程度极高的胜利老油区依然可以解决一些石油地质问题,尤其与其它物探资料的联合解释取得了明显的地质效果。依据高青测区所取得的高精度重力资料,基本查清了区内的古潜山分布规律,为下一步古潜山油气藏的勘探部署提供了重要的参考资料。     

资源三号卫星影像纠正的方法研究

影像的应用前提是影像处理,大气校正可消除大气和光照等因素对地物发射的影响。在此基础上,介绍几种常用的几何纠正方法,可消除系统和非系统因素引起的影像几何变形。本文通过对资源三号卫星的基本参数信息和相对应影像数据特点的介绍,先通过大气校正,校正后的影像分别使用几何纠正的几种方法,并通过各种方法的计算量、实用性以及相应的精度,对比分析各种方法在处理这种数据时的优越性。     

SMAP卫星海表盐度产品的检验与校正

In this study, sea surface salinity(SSS) Level 3(L3) daily product derived from soil moisture active passive(SMAP)during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity(SMOS) and in-situ measurements. Generally, the root mean square error(RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature(SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.     

基于机器学习的海洋环境预报订正方法研究

结合中尺度数值模式 WRF 预报数据和 ERA5 再分析资料,利用机器学习方法对 WRF 预报场的风场、温度、气压进行预报订正。采用 ERA5 作为真值,与原始 WRF 预报相比,利用随机森林模型可以将预报结果整体均方根误差降低 44%以 上,利用深度神经网络模型可以将预报结果整体均方根误差降低 34%以上。通过随机森林模型实验得到不同输入特征对预报要素的影响程度,分析了关键的预报订正因子。     

基于Kongsberg EM多波束的Snippet数据处理方法

多波束声呐记录的海底后向散射片段(Snippet)数据处理成角度响应曲线和地理编码(Mosaic)图像可以 帮助识别海底底质类型和反映地貌形态,这一过程包括辐射校正、角度响应改正(AVG)和几何地理编码,但不同的多波束系统硬件在辐射校正和角度响应改正方法上存在差异且传统处理方法忽略了声呐系统本身的指向性模型随时间变化的事实。以声呐方程为基础,针对Kongsberg EM 多波束系统提出了一套完整的Snippet数据处理流程,并分析了各步骤中存在的可变性,给出了每一步的处理建议,最后将此方法应用于EM2040浅水多波束实测数据,并验证了该方法的有效性和实用性。     

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

山地叶面积指数反演理论、方法与研究进展

叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数,在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段,当前LAI产品主要基于遥感数据反演得到,但是多数LAI产品算法并未考虑地形特征的影响,导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响,其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法,综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展,总结了目前山地LAI遥感反演存在的问题,并讨论了未来研究的发展趋势。     

台湾海峡MERIS数据大气校正研究

根据MERIS(Medium Resolution Imaging Spectrometer,中等分辨率成像光谱仪)L1B产品特点,以台湾海峡为研究区域,开展MERIS数据大气校正研究。在阐述水色遥感数据大气校正原理的基础上,首先进行水体像元提取和辅助参数空间内插,获取大气校正所需的数据集。接着进行耀斑反射率计算和校正并利用加倍法精确计算瑞利散射。最后针对台湾海峡水体为清洁到轻度浑浊,提出先区分水体类别,再分别利用epsilon指数外推法进行一类水体气溶胶散射计算,和利用神经网络法计算二类水体离水反射率即ρw的方法。对大气校正的结果进行ρw负值像元数统计和光谱分析表明,该方法不仅降低ρw为负值的像元数,而且使大气校正后水体的光谱曲线同现场数据保持较好的一致性。     

不同光程放大校正因子对中国近海典型水体吸收光谱的影响

在总结各种文献对光程放大校正因子研究的基础上,通过颗粒物吸收系数量值和吸收光谱谱形两个方面,对不同光程放大校正因子对中国近海典型水体颗粒物吸收光谱的影响进行了比较。当ODf-ODf(750 nm)的吸光度在0.005～0.5的范围时,不同光程放大校正因子得到的悬浮颗粒物吸光度值差异较大。