Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.     

不同地形条件地面同步光谱定标场对比研究

利用美国ASD公司的FieldSpec Pro FR地面波谱仪,选择5°视场角探头和135cm探头距测试地物高度,开展0°、5°、10°、15°、20°等五种不同地形坡度明、暗地物自然定标场地面波谱测试。通过对比研究不同地形坡度具代表性、能真实反映被测试目标平均自然性的各波谱测试点波谱曲线特征,得出0°、5°地形坡度获取的波谱曲线连续、平滑,信噪比高,没有混入大气水气吸收、仪器噪声等因素导致的剧烈波动、跳跃现象或尖锐锯齿状噪声,波谱特征明显,吸收峰最小值位置清晰可辨,各波谱测试点波谱曲线变化幅度小,整体反射率值变化小,Fe~(2+)、Fe~(3+)、Al-OH、Mg-OH、CO_3~(2-)等分子基团与离子波谱的诊断性波谱特征位置清晰可辨。因此0°和5°坡度地形条件获取的地面同步自然场光谱定标波谱曲线,可以满足航空高光谱遥感数据的大气校正、光谱重建和空、地回归分析等所需的地面波谱数据要求。     

云南姚安铅银矿床假白榴石斑岩空间分布和组构特征

姚安铅银矿床位于金沙江-哀牢山断裂带东侧的富碱斑岩带内,主要发育正长斑岩、粗面岩、假白榴石斑岩、含斑正长细晶岩等一套杂岩体,其中正长斑岩为主要容矿围岩。通过野外地质勘查、显微镜鉴定、电子探针及X射线微区衍射分析,对假白榴石斑岩的空间分布、岩石矿物学特征进行了综合研究,结果表明,姚安铅银矿床假白榴石斑岩多以脉状、角砾状或呈小岩枝产出,出露在正长斑岩质火山角砾岩的外围,主要分布在角砾岩型铅银矿(化)体的南西侧、F4断裂的北东盘,在空间上与角砾岩筒及外围断裂有关;该矿床的假白榴石具两个世代,早期为灰绿色浑圆粒状假白榴石斑岩的假白榴石斑晶,富Al,晚期为灰-灰白色四角三八面体假白榴石斑岩的假白榴石斑晶,富Si、Na,二者均蚀变成钾长石、高岭石、石英等含水富硅矿物;姚安假白榴石斑岩形成于印度板块与欧亚大陆俯冲碰撞后的板内拉张环境;假白榴石斑岩形成晚于粗面岩、正长斑岩,早于含斑正长细晶岩;假白榴石斑岩与铅银矿(化)体无直接成因联系。     

Curvedness feature constrained map matching for low-frequency probe vehicle data

Map matching method is a fundamental preprocessing technique for massive probe vehicle data. Various transportation applications need map matching methods to provide highly accurate and stable results. However, most current map matching approaches employ elementary geometric or topological measures, which may not be sufficient to encode the characteristic of realistic driving paths, leading to inefficiency and inaccuracy, especially in complex road networks. To address these issues, this article presents a novel map matching method, based on the measure of curvedness of Global Positioning System (GPS) trajectories. The curvature integral, which measures the curvedness feature of GPS trajectories, is considered to be one of the major matching characteristics that constrain pairwise matching between the two adjacent GPS track points. In this article, we propose the definition of the curvature integral in the context of map matching, and develop a novel accurate map matching algorithm based on the curvedness feature. Using real-world probe vehicles data, we show that the curvedness feature (CURF) constrained map matching method outperforms two classical methods for accuracy and stability under complicated road environments.     

基于改进K-SVD字典学习方法的地震数据去噪

为实现更好的地震数据去噪技术,笔者引入一种新的算法:快速迭代收缩阀值法(FISTA),通过FISTA和K-奇异值分解(K-SVD)不断迭代更新K-SVD字典,利用更新得到的K-SVD字典对地震数据进行稀疏表示,去除稀疏系数中较小的数值,使数据中的随机噪声得到压制。对层状模型合成地震记录,Marmousi模型合成地震记录以及实际地震数据进行对比实验,得出FISTA算法较OMP算法能更好地提高地震数据的信噪比,同时有效地保护了反射信号。     

基于面向对象分类的稀土开采区遥感信息提取方法研究

离子吸附型稀土矿是我国宝贵的矿产资源,运用遥感影像分类技术提取稀土开采区可以准确地实现对稀土开采状况的监测,但仅利用光谱信息往往难以保证分类精度。本文以江西寻乌稀土矿区为研究区,以IKONOS影像为数据源,应用面向对象分类方法提取了稀土开采区的遥感信息。针对稀土开采区的分布特点,选择基于边缘的分割算法进行影像分割;结合地形信息、光谱信息及几何信息建立规则集,进行特征提取;最后采用隶属度函数法实现面向对象分类,并与传统的光谱角填图分类进行对比分析。研究结果表明,面向对象分类法提取稀土开采区的总体精度为92.49%,Kappa系数为0.857 6,与传统监督分类方法相比有了很大的提高。     

波浪在珊瑚岸礁礁坪上传播变形的数值研究

波浪的传播变形对珊瑚岛礁附近的营养物质输送以及近岸珊瑚沙运动都起着关键的作用,同时也是礁盘顶部建筑物在设计和施工过程中需要重点考虑的因素,因此,对波浪在礁坪上的传播变形进行研究具有重要意义。利用FUNWAVE 2.0数值模型模拟了不规则波浪在不同形状岸礁上的传播变形过程,讨论了礁前斜坡坡度以及珊瑚底面粗糙程度对波浪在礁坪上传播变形的影响。结果显示,随着礁坡斜率增大,不对称度参数绝对值的增长趋势逐渐变缓,波浪在光滑礁坪上的不对称度绝对值要略大于粗糙底面上的值,而不同地形下计算得到的波浪偏度参数则基本相同。说明礁坡坡度对波浪的不对称度特征具有显著影响,礁坪粗糙度的影响则相对较小,而上述因素对波浪偏度的影响则完全可以忽略。给出了考虑这两个影响因素的礁坪区域内波浪偏度和不对称度经验公式。     

小极距高密度电法在城镇供水管漏水检测中的应用

城市地下供水管破损漏水检测,一般使用听音工具和设备来进行,这种需要漏水点传出声音的检测方式,效果往往并不理想。使用一种新的检测方法,无需利用声音,而是通过供水管漏水造成地层电性变化这一特征,使用小极距高密度电法查明管道漏水点位置。该检测方式不仅解决了常规方法难以查明的供水管漏水问题,而且拓展了小极距高密度电法的应用空间。     

Geometric-constrained multi-view image matching method based on semi-global optimization

Targeting at a reliable image matching of multiple remote sensing images for the generation of digital surface models, this paper presents a geometric-constrained multi-view image matching method, based on an energy minimization framework. By employing a geometrical constraint, the cost value of the energy function was calculated from multiple images, and the cost value was aggregated in an image space using a semi-global optimization approach. A homography transform parameter calculation method is proposed for fast calculation of projection pixel on each image when calculating cost values. It is based on the known interior orientation parameters, exterior orientation parameters, and a given elevation value. For an efficient and reliable processing of multiple remote sensing images, the proposed matching method was performed via a coarse-to-fine strategy through image pyramid. Three sets of airborne remote sensing images were used to evaluate the performance of the proposed method. Results reveal that the multi-view image matching can improve matching reliability. Moreover, the experimental results show that the proposed method performs better than traditional methods.     

A geometric-based approach for road matching on multi-scale datasets using a genetic algorithm

Object matching is used in various applications including conflation, data quality assessment, updating, and multi-scale analysis. The objective of matching is to identify objects referring to the same entity. This article aims to present an optimization-based linear object-matching approach in multi-scale, multi-source datasets. By taking into account geometric criteria, the proposed approach uses real coded genetic algorithm (RCGA) and sensitivity analysis to identify corresponding objects. Moreover, in this approach, any initial dependency on empirical parameters such as buffer distance, threshold of spatial similarity degree, and weights of criteria is eliminated and, instead, the optimal values for these parameters are calculated for each dataset. Volunteered geographical information (VGI) and authoritative data with different scales and sources were used to assess the efficiency of the proposed approach. According to the results, in addition to an efficient performance in various datasets, the proposed approach was able to appropriately identify the corresponding objects in these datasets by achieving higher F-Score.