An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.     

A global optimization approach to roof segmentation from airborne lidar point clouds

This paper presents a global plane fitting approach for roof segmentation from lidar point clouds. Starting with a conventional plane fitting approach (e.g., plane fitting based on region growing), an initial segmentation is first derived from roof lidar points. Such initial segmentation is then optimized by minimizing a global energy function consisting of the distances of lidar points to initial planes (labels), spatial smoothness between data points, and the number of planes. As a global solution, the proposed approach can determine multiple roof planes simultaneously. Two lidar data sets of Indianapolis (USA) and Vaihingen (Germany) are used in the study. Experimental results show that the completeness and correctness are increased from 80.1% to 92.3%, and 93.0% to 100%, respectively; and the detection cross-lap rate and reference cross-lap rate are reduced from 11.9% to 2.2%, and 24.6% to 5.8%, respectively. As a result, the incorrect segmentation that often occurs at plane transitions is satisfactorily resolved; and the topological consistency among segmented planes is correctly retained even for complex roof structures.     

An evaluation of extrapolation techniques for the short‐term prediction of rain amounts

Abstract

Twenty‐seven radar cells from the Tropical Atlantic observed during GATE were followed and measurements of their fluxes and areas for initial time increments T₀ were fitted to various extrapolation schemes. The extrapolation procedure that gave the smallest error inforecasting the changes influx and area, was found to be the linear one and the optimum increment T₀ was about 30 min. However, even though these techniques have the advantage of establishing a trend in the behaviour of the flux and area with time, a comparison of the forecast errors from the linear extrapolation scheme with those from the “status quo” (persistence) assumption shows little if any improvement.

A technique including both cell motion and internal changes influx and area of the rain cells was developed to evaluate the accuracy of rain accumulation forecasts. It was found that the errors generated by the “status quo” assumption were of the order of 77% for a 2‐h forecast with little improvement by allowing for the extrapolation of area and flux.     

一种基于倾斜摄影测量点云密度自适应分割的建筑物边界提取方法

针对倾斜摄影场景中建筑物单体化问题,本文提出了基于倾斜摄影测量点云数据的建筑物识别和边界提取自动化算法。首先,对点云进行预处理,去除地面点和噪声点;然后,对点云进行二维栅格化处理,按间隔距离预分割;最后,结合改进的大津算法和区域增长算法,从预分割点云识别其中的建筑物,并提取建筑物边界点。从广东省江门市和湛江市选取两处试验区域对算法进行测试,结果表明:区域内建筑物点云均能准确被分割识别,建筑物边界提取准确度分别为87.8%与92.3%,说明本文提出的方法对于倾斜摄影测量建筑物识别和边界提取的适用性较强。     

层状云微物理属性垂直分布的季节变化——以新疆地区为例

利用CloudSat卫星资料2B-CLDCLASS和2B-CWC-RVOD数据集,分别揭示了层云与层积云的云粒子等效半径、数浓度及含水量等微物理属性的垂直分布特征及其季节变化规律。结果表明：层云所在的云层高度比层积云高,特别是春季,层云可以延伸到11.0 km处,而层积云云层所在高度最高出现在夏季,最高延伸到8.5 km处。层云中的冰水分布在1.0~11.0 km,液态水分布在0~9.0 km,而层积云的冰水和液态水均分布在0~9.0 km。层云与层积云的冰粒子等效半径、水云粒子数浓度、液态水含量呈现随云层高度的增加而减小的趋势,冰粒子数浓度随云层高度增加呈增大趋势,冰水含量呈峰值分布,峰值出现在6~10 km范围内。各季节层积云粒子等效半径、粒子数浓度及水含量的最大值均大于层云的对应值,从这方面看,层积云更适合作人工增水对象。     

一次飞机冷云增雨作业效果检验

最近60多年,全球范围内广泛开展了人工增雨作业,但人工增雨效果检验一直是个难题。传统上,利用雨量计和目标/对比区统计数据评估人工增雨效果,结果大多不确定。对一次人工增雨作业而言,从科学上给出令人信服的效果检验更是没有好的解决方案。2017年3月19日,陕西省实施业务飞机冷云增雨作业播撒含有750 g碘化银（AgI）的催化剂,播撒线长125 km。作业后卫星、雷达观测到一条与播云线对应的清晰的云迹线,地面雨滴谱仪观测到相应的雨强、雨滴数浓度、雨滴直径增大,表明播云使云体产生了增雨响应。针对这次增雨过程,从连片雷达回波中分离增雨作用造成的回波增强带（增雨影响回波）和确定了自然降水回波强度,建立增雨影响回波强度（Z）与地面雨强（I）的拟合关系（Z-I关系）,定量研究人工增雨的时、空演变。结果表明:（1）增雨影响时间约4 h,增雨影响回波区域（增雨影响区）面积为5448 km2。该区累计降雨总量和增雨总量分别为1.518×106 m3和8.04×105 m3,增雨影响区内增雨率达53%。（2）总降雨量、增雨量、自然降雨量随时间先增后减,总降雨量与增雨量的峰值同步,两者峰值都早于自然降雨峰值;催化后146 min （04时47分,世界时,下同）,每6 min增雨量达到最大,为4.9×104 m3;催化后174 min （05时15分）,增雨雷达回波面积达到最大（1711 km2）,面积峰值滞后增雨量峰值出现。（3）增雨影响区位于播撒线下游,呈条带状;区域内总降雨量空间分布为中间大边缘小,与增雨量空间分布一致。（4）此次增雨作业改变了降雨时、空分布,促进降雨形成,增加了地面降雨量。      

三维点云室内平面要素提取与优化

室内平面要素的准确提取与关系恢复是室内模型自动化语义重建的重要基础,本文提出了一种面向复杂三维点云的室内平面要素提取与优化方法。该方法首先利用区域增长和RANSAC平面混合分割方法分割室内点云数据;其次利用室内语义部件的空间位置信息及包围盒和法向量信息,对分割后的平面进行平面要素的精确提取;然后对提取的墙面进行优化,实现共享墙面的合并,解决室内墙面冗余的问题;最后利用门与墙的空间位置信息,恢复门墙关联关系。试验部分采用了两组试验数据：一组是深圳大学某层教学楼的激光点云数据,另一组是国际摄影测量与遥感学会（ISPRS）的标准数据,通过对试验结果进行评估,验证了本文方法的有效性和可靠性。     

利用多元LBP特征自动提取城市道路边界

车载移动测量系统可采集高精度道路三维点云数据,为道路边界自动化提取提供了支撑.为解决车载激光点云中城市道路边界点云提取困难问题,本文引入局部二值模式LBP(Local Binary Pattern),针对各类城市道路边界特征,设计了高度LBP、高程离散度LBP和空间形状LBP3种改进算子;构建多元LBP特征语义识别模型...     

Determination of cirrus radiative parameters from combination between active and passive remote sensing measurements during FRENCH/DIRAC 2001

In the context of the next AQUA Train satellite experiment, airborne measurements were carried out to simulate satellite measurements. They were conducted between September 25 and October 12, 2001, off the coast of southern France over the Atlantic Ocean and over the Mediterranean Sea, respectively. During the intensive Field Radiation Experiment on Natural Cirrus and High-level clouds (FRENCH/DIRAC 2001), natural ice clouds were sampled from in situ and remote sensing measurements. On October 5 and 7, 2001, cirrus cloud decks were described by a complete data set acquired by: (i) in situ microphysical instruments onboard the TBM-700 aircraft: PMS probe, and Polar Nephelometer (ii) and downward-looking radiative instruments onboard the Mystère 20 aircraft: an infrared radiometer, a lidar, a visible imager with polarisation capabilities, and a middle infrared radiometer. Moreover, classical thermodynamical measurements were carried out onboard the Mystère 20. Mean microphysical characteristics of cirrus deck are derived from interpretation of remote sensing measurements. These properties are compared with those derived from in situ microphysical measurements in order to evaluate the radiative impact of natural cirrus clouds.     

登陆台风Winnie(1997)的数值模拟研究Ⅰ:结果检验和云系的模拟

用非静力平衡的中尺度模式MM5 (V2 )对 1997年 11号台风Winnie在登陆后演变为温带气旋的过程进行了48h模拟。结果表明 :MM 5不仅比较好地模拟出台风在陆地上的移动路径及其产生的降水 ,而且成功地模拟出了台风登陆后次中心的产生。利用模拟大气中的水物质 (云水、雨水、冰晶、雪水和霰 )模拟了台风云图 ,很好地展示了在卫星实际观测的红外云图上 ,Winnie台风在登陆后其云系的结构从热带气旋的螺旋结构到温带气旋的锋面云系结构的转变过程。因此对Winnie台风的数值模拟可以作为深入研究台风登陆后从热带气旋演变为温带气旋的变性过程的基础。