Integration of full-waveform LiDAR and hyperspectral data to enhance tea and areca classification

Hyperspectral image and full-waveform light detection and ranging (LiDAR) data provide useful spectral and geometric information for classifying land cover. Hyperspectral images contain a large number of bands, thus providing land-cover discrimination. Waveform LiDAR systems record the entire time-varying intensity of a return signal and supply detailed information on geometric distribution of land cover. This study developed an efficient multi-sensor data fusion approach that integrates hyperspectral data and full-waveform LiDAR information on the basis of minimum noise fraction and principal component analysis. Then, support vector machine was used to classify land cover in mountainous areas. Results showed that using multi-sensor fused data achieved better accuracy than using a hyperspectral image alone, with overall accuracy increasing from 83% to 91% using population error matrices, for the test site. The classification accuracies of forest and tea farms exhibited significant improvement when fused data were used. For example, classification results were more complete and compact in tea farms based on fused data. Fused data considered spectral and geometric land-cover information, and increased the discriminability of vegetation classes that provided similar spectral signatures.     

Optimal fusion of optical and SAR high-resolution images for semiautomatic building detection

Building detection from different high-resolution aerial and satellite images has been a notable research topic in recent decades. The primary challenges are occlusions, shadows, different roof types, and similar spectral behavior of urban covers. Integration of different data sources is a solution to supplement the input feature space and improve the existing algorithms. Regarding the different nature and unique characteristics of optical and radar images, there are motivations for their fusion. This paper is aimed to identify an optimal fusion of radar and optical images to overcome their individual shortcomings and weaknesses. For this reason, panchromatic, multispectral, and radar images were first classified individually, and their strengths and weaknesses were evaluated. Different feature-level fusions of these data sets were then assessed followed by a decision-level fusion of their results. In both the feature and decision levels of integration, artificial neural networks were applied as the classifiers. Several post-processing methods using normalized different vegetation index, majority filter, and area filter were finally applied to the results. Overall accuracy of 92.8% and building detection accuracy of 89.1% confirmed the ability of the proposed fusion strategy of optical and radar images for building detection purposes.     

基于SIFT特征和改进融合策略的图像拼接算法

为了消除图像拼接中的缝隙,本文提出一种改进的图像拼接算法。该算法首先利用经典的SIFT算子提取特征点,接着建立两幅图像的映射关系,利用RANSAC算法进行特征匹配点提纯,最后重叠区域的像素融合采用改进的加权平均算法。实验表明,该算法可以很好地消除重叠区域的拼接缝隙。     

滑坡多点数据融合中的多传感器目标跟踪技术应用

目前的滑坡预报模型只能利用一个关键监测点的监测数据,而不能同时利用多个监测点的监测信息。为了克服选取关键监测点的人为性以及充分利用滑坡多个监测点的信息,针对滑坡具有机动的特性,提出了利用多传感器目标跟踪融合技术来处理滑坡多个点的监测数据的方法,分析了其可行性,并用实例说明了提出的方法在实际应用中是有效的。     

Petrogenesis and evolution of late Mesozoic granitic magmatism in the Hohhot metamorphic core complex,Daqing Shan,North China

Late Mesozoic granitoid plutons of four distinct ages intrude the lower plate of the Hohhot metamorphic core complex along the northern margin of the North China craton. The plutons belong to two main groups: (1) Group I, deformed granitoids (148 and 140 Ma subgroups) with high Sr, LREE, and Na₂O, low Y and Yb contents, high Sr/Y and La/Yb ratios, weak or no Eu anomalies, low Rb/Ba ratios, similar initial ⁸⁷Sr/⁸⁶Sr values (0.7064–0.7071) and low Mg^# (<37 mostly, 100?×?molar MgO/MgO + FeO _t ); (2) Group II, non-deformed granitoids (132 and 114 Ma subgroups) with low Sr, relatively low Na₂O, high Y and Yb contents, pronounced negative Eu anomalies, high Rb/Ba ratios, and initial ⁸⁷Sr/⁸⁶Sr values (0.7098–0.7161). The two groups share geochemical similarities in ?_Nd(t) (–11.3 to –15.4) and T _DM2 ages (1.85–2.18 thousand million years) as well as Hf isotopic ratios in zircons. Geochemical modelling (using the MELTS code) suggests that similar sources but different depths of magma generation produced the early, high-pressure low-Mg adakitic granitoids and late, low-pressure granitoids with A-type characteristics. The early granitoids likely represent a partially melted, deep-seated, thickened lower continental crust that involved a minor contribution from young materials, whereas the later group partially melted at shallower depths. This granitic magmatic evolution coincided with the tectonic transition from crustal contraction to extension.     

D-S证据理论在地震属性中的应用

在利用复地震道分析技术提取出地震信号局部变化的三瞬属性(瞬时振幅;瞬时相位和瞬时频率)的基础上,采用D—S改进算法对三瞬属性进行多传感器的证据融合,得到三种属性所占的权重,并根据相应权重,经过数据融合可以得到最终融合结果。这样有效融合了三种地震属性中有利于进行地震薄互层分析的地震信息,与单一的复地震道分析相比,更能有效地反映出地震信号的局部变化。最后通过对实际的地震资料进行了处理,发现D—S信息融合技术体现的地震波属性的变化,比单一属性更能清楚地反映出地质体的地质现象及表达的沉积过程。     

基于缓冲区融合的临近节点检查方法研究

临近节点是影响矢量数据质量的关键问题.本文针对数据维度提升带来的高维信息这一特征,将要素节点升级为节点缓冲区,设计实现了基于要素节点缓冲区融合的临近节点检查方法,通过多源数据合并、要素转节点、相同节点融合、缓冲以及缓冲区融合等步骤获得要素节点缓冲区融合面,最后通过面积容差判定出临近节点所在范围.实验证明,算法通用性强,...     

基于多传感器的民航客机定位

为获取民航客机落地前的高精度位置,本文探讨了一种基于多传感器的民航客机高精度定位方法。在飞机飞行过程中,机载数据记录器会记录整个飞行过程中GPS接收机、无线电高度表、惯性导航系统等传感器数据;在飞机落地后,利用无线电高度表数据、GPS数据进行位置差分,再利用惯性导航数据进一步优化,可大幅提高落地前垂直定位精度。结果表明,通过本文方法在机场跑道入口前2 km,飞机定位精度大幅提高,垂直定位精度可以达米级,满足事后调查需求。     

结合扩展卡尔曼滤波与基于点线的最近点迭代扫描匹配算法的机器人位姿自适应估计

针对机器人利用单一位姿估计方法累积误差大、精度低的问题,本文提出了一种基于扩展卡尔曼滤波算法(EKF)和基于点线的最近点迭代扫描匹配算法(PL-ICP)的机器人位姿自适应估计方法。为了减少轮式里程计造成的累积误差,利用Mahony算法对陀螺仪和加速度计进行姿态解算,进而基于扩展卡尔曼滤波融合轮式里程计初步估计机器人位姿;为了减少轮子变形、打滑等对机器人位姿的影响,利用PL-ICP点云匹配算法构建单线激光里程计,对机器人位姿再次进行估计;为了提高位姿估计的准确度,提出了一种基于两种位姿总均方差和前后时刻位姿误差构建累积误差的自适应修正算法,通过分析两种位姿总均方差及前后时刻位姿误差,得到全局最优权重因子和局部动态权重因子,实现累积误差修正因子的自适应调整,得到机器人更精确的位姿估计。试验结果表明,该方法可对机器人的位姿累积误差进行修正,显著提高机器人的位姿估计精度。     

YOLO v4框架下Multi-Patch多帧增量式交通视频目标检测

提升目标检测模型的泛化能力是计算机视觉领域的研究热点和关键难点。本文提出了一种Multi-Patch方法和多帧增量式预测策略,提升了不同场景下交通视频目标检测的稳健性,有效解决了目标尺度多变导致的视频中目标召回率低的问题。根据视频分辨率和目标尺寸,基于Multi-Patch方法自动将视频帧分割成最佳输入尺寸,使用YOLO v4神经网络并关联连续帧的上下文信息,采用增量式预测策略降低视频目标检测的漏检率,提升不同场景下视频目标的检测置信度得分和召回率。采集不同拍摄条件下的交通视频,验证该方法的有效性。试验结果表明,本文提出的目标检测方法召回率在80%以上,置信度平均得分在0.84以上。