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International Journal of Digital Earth 2017年第3期10卷 307-323页

作者： Yanjun Su Qin Ma Qinghua Guo Sierra Nevada Research Institute School of EngineeringUniversity of California at MercedMercedCAUSA State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijingPeople’s Republic of China

Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest ecosystem.There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution.In this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(lidar)data.Accurate tree height estimates within these airborne lidar boundaries were first computed based on in situ measurements,and then these airborne lidar-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)footprints.Finally,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate data.The results show that our fine-resolution SN tree height product has a good correspondence with field measurements.The coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.

关键词： Tree height Sierra Nevada lidar integration fine resolution

Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event lidar data

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International Journal of Digital Earth 2018年第12期11卷 1241-1268页

作者： Milad Janalipour Ali Mohammadzadeh Faculty of Geodesy&Geomatics Engineering K.N.Toosi University of TechnologyTehranIran

Building damage maps after disasters can help us to better manage the rescue operations.Researchers have used Light Detection and Ranging(lidar)data for extracting the building damage maps.For producing building damage maps from lidar data in a rapid manner,it is necessary to understand the effectiveness of features and classifiers.However,there is no comprehensive study on the performance of features and classifiers in identifying damaged areas.In this study,the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated.In the proposed method,at first,a pre-processing stage was utilized to apply essential processes on post-event lidar data.Second,textural features were extracted from the pre-processed lidar data.Third,fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents.The proposed method was tested across three areas over the 2010 Haiti earthquake.Three building damage maps with overall accuracies of 75.0%,78.1%and 61.4%were achieved.Based on outcomes,the fuzzy inference systems were stronger than random forest,bagging,boosting and support vector machine classifiers for detecting damaged buildings.

关键词： ANFIS model backpropagation learning building damage detection fuzzy system generation strategies lidar texture analysis

Using lidar and ground truth for landslide recognition and characterization of geotechnical and morphological parameters in sedimentary rocks,a case study in Northern Bavaria (Germany)

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Journal of Mountain Science 2017年第8期14卷 1513-1525页

作者： WIEDENMANN Johannes ROHN Joachim MOSER Michael GeoZentrum Nordbayern Department of Applied GeologyUniversity of Erlangen-Nürnberg

The use of lidar-derived shaded relief maps led to the identification of all potential landslide areas in a hilly region in Northern Bavaria(1590 km^2), Germany. Every possible structure was investigated by field investigation which resulted in a detailed database of 1002 landslides within the study area. The analysis of geological, lithological, topographical and morphological properties(spatial ratio, lithological and geological setting, length/width distribution, material properties and slope angle) revealed characteristic appearances of the landslides and possible relationships between different aspects. Strong relations between the lithological setting, spatial ratio of the mapped landslides and distribution of slope angle could be observed. This study shows the value of high-resolution shaded relief maps for detecting and mapping landslides in a large area with comparatively little work and time in comparison to the traditional approach to mapping. It reveals that many landslides were not known before. Landslides are much more common in Northern Bavaria and have a higher influence on the denudation rate of the Franconian Alb than expected before.

关键词： Landslides lidar Appearance Spatialratio Material properties Bavaria

Development and comparison of aerial photograph aided visualization pipelines for lidar datasets

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International Journal of Digital Earth 2015年第8期8卷 656-677页

作者： Suddhasheel Ghosh Bharat Lohani Department of Civil Engineering MGM’s Jawaharlal Nehru Engineering CollegeAurangabadIndia Department of Civil Engineering Indian Institute of Technology KanpurKanpurIndia

Light Detection and Ranging(lidar)technology generates dense and precise threedimensional datasets in the form of point clouds.Conventional methods of mapping with airborne lidar datasets deal with the process of classification or feature specific segmentation.These processes have been observed to be time-consuming and unfit to handle in scenarios where topographic information is required in a small amount of time.Thus there is a requirement of developing methods which process the data and reconstruct the scene in a small amount of time.This paper presents several pipelines for visualizing lidar datasets without going through classification and compares them using statistical methods to rank these processes in the order of depth and feature perception.To make the comparison more meaningful,a manually classified and computer-aided design(CAD)reconstructed dataset is also included in the list of compared methods.Results show that a heuristic-based method,previously developed by the authors perform almost equivalent to the manually classified and reconstructed dataset,for the purposes of visualization.This paper makes some distinct contributions as:(1)gives a heuristics-based visualization pipeline for lidar datasets,and(2)presents an experimental design supported by statistical analysis to compare different pipelines.

关键词： lidar mining feature extraction visualization experiment design nonparametric statistics

Analysis of the factors affecting lidar DTM accuracy in a steep shrub area

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International Journal of Digital Earth 2011年第6期4卷 521-538页

作者： J.Estornell L.A.Ruiz B.Vela´zquez-Martı´ T.Hermosilla Departamento de Ingenierı´a Cartogra´fica Geodesia y Fotogrametrı´aUniversidad Polite´cnica de ValenciaCamino de Vera s/nValencia 46022Spain Departamento de Ingenierı´a Rural y Agroalimentaria Universidad Polite´cnica de ValenciaValenciaSpain

The creation of a quality Digital Terrain Model(DTM)is essential for representing and analyzing the Earth in a digital form.The continuous improvements in the acquisition and the potential of airborne Light Detection and Ranging(lidar)data are increasing the range of applications of this technique to the study of the Earth surface.The aim of this study was to determine the optimal parameters for calculating a DTM by using an iterative algorithm to select minimum elevations from lidar data in a steep mountain area with shrub vegetation.The parameters were:input data type,analysis window size,and height thresholds.The effects of slope,point density,and vegetation on DTM accuracy were also analyzed.The results showed that the lowest root mean square error(RMSE)was obtained with an analysis window size of 10 m,5 m,and 2.5 m,rasterized data as input data,and height thresholds equal to or greater than 1.5 m.These parameters showed a RMSE of 0.19 m.When terrain slope varied from 010%to 5060%,the RMSE increased by 0.11 m.The RMSE decreased by 0.06 m when point density was increased from 4 to 8 points/m2,and increased by 0.05 m in dense vegetation areas.

关键词： lidar digital earth Digital Terrain Model(DTM) filtering 3-D representation

Building segmentation and modeling from airborne lidar data

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International Journal of Digital Earth 2015年第9期8卷 694-709页

作者： Yong Xiao Cheng Wang Jing Li Wuming Zhang Xiaohuan Xi Changlin Wang Pinliang Dong Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingChina State Key Laboratory of Remote Sensing Science School of GeographyBeijing Normal UniversityBeijingChina Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijingChina Department of Geography University of North TexasDentonTXUSA

Due to the high accuracy and fast acquisition speed offered by airborne Light Detection and Ranging(lidar)technology,airborne lidar point clouds have been widely used in three-dimensional building model reconstruction.This paper presents a novel approach to segment building roofs from point clouds using a Gaussian mixture model in which buildings are represented by a mixture of Gaussians(MoG).The Expectation-Maximization(EM)algorithm with the minimum description length(MDL)principle is employed to obtain the optimal parameters of the MoG model for separating building roofs.To separate complete planar building roofs,coplanar Gaussian components are merged according to their distances to the corresponding planes.In addition,shape analysis is utilized to remove nonplanar objects caused by trees and irregular artifacts.Building models are obtained by combining segmented planar roofs,topological relationships,and regularized building boundaries.Roof intersection segments and points are derived by the segmentation results,and a rasterbased regularization method is employed to obtain geometrically correct and regular building models.Experimental results suggest that the segmentation method is able to separate building roofs with high accuracy while maintaining correct topological relationships among roofs.

关键词： lidar roof segmentation mixture of Gaussians reconstruction boundary regularization

Post-earthquake assessment of building damage degree using lidar data and imagery

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Science China(Technological Sciences) 2008年第S2期51卷 133-143页

作者： LI ManChun1,CHENG Liang1,2,GONG JianYa2,LIU YongXue1,CHEN ZhenJie1,LI FeiXue1,CHEN Gang1,CHEN Dong1 & SONG XiaoGang3 1 Department of Geographic Information Science,Nanjing University,Nanjing 210093,China 2 State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China 3 State Key Laboratory of Earthquake Dynamics,Institute of Geology,China Earthquake Administration,Beijing 100029,China Department of Geographic Information Science Nanjing University Nanjing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China State Key Laboratory of Earthquake Dynamics Institute of Geology China Earthquake Administration Beijing China

Various methods have been developed to detect and assess building's damages due to earthquakes using remotely sensed data.After the launch of the high resolution sensors such as IKONOS and QuickBird,it becomes realistic to identify damages on the scale of individual building.However the low accuracy of the results has often led to the use of visual interpretation techniques.Moreover,it is very difficult to estimate the degree of building damage(e.***.slight damage,moderate damage,or severe damage) in detail using the existing methods.Therefore,a novel approach integrating lidar data and high resolution optical imagery is proposed for evaluating building damage degree quantitatively.The approach consists of two steps:3D building model reconstruction and rooftop patch-oriented 3D change detection.Firstly,a method is proposed for automatically reconstructing 3D building models with precise geometric position and fine details,using pre-earthquake lidar data and high resolution imagery.Secondly,focusing on each rooftop patch of the 3D building models,the pre- and post-earthquake lidar points belonging to the patch are collected and compared to detect whether it was destroyed or not,and then the degree of building damage can be identified based on the ratio of the destroyed rooftop patches to all rooftop patches.The novelty of the proposed approach is to detect damages on the scale of building's rooftop patch and realize quantitative estimation of building damage degree.

关键词： post-earthquake assessment building damage degree rooftop patch lidar imagery

Shearlet-Based Structure-Aware Filtering for Hyperspectral and lidar Data Classification

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Journal of Remote Sensing 2021年第1期2021卷 237-261页

作者： Sen Jia Zhangwei Zhan Meng Xu College of Computer Science and Software Engineering Shenzhen UniversityShenzhenChina Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the Ministry of Natural Resources Shenzhen UniversityShenzhenChina SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for SocietyShenzhenChina

The joint interpretation of hyperspectral images(HSIs)and light detection and ranging(lidar)data has developed rapidly in recent years due to continuously evolving image processing technology.Nowadays,most feature extraction methods are carried out by convolving the raw data with fixed-size filters,whereas the structural and texture information of objects in multiple scales cannot be sufficiently exploited.In this article,a shearlet-based structure-aware filtering approach,abbreviated as ShearSAF,is proposed for HSI and lidar feature extraction and classification.Specifically,superpixel-guided kernel principal component analysis(KPCA)is firstly adopted on raw HSIs to reduce the dimensions.Then,the KPCA-reduced HSI and lidar data are converted to the shearlet domain for texture and area feature extraction.In contrast,superpixel segmentation algorithm utilizes the raw HSI data to obtain the initial oversegmentation map.Subsequently,by utilizing a well-designed minimum merging cost that fully considers spectral(HSI and lidar data),texture,and area features,a region merging procedure is gradually conducted to produce a final merging map.Further,a scale map that locally indicates the filter size is achieved by calculating the edge distance.Finally,the KPCA-reduced HSI and lidar data are convolved with the locally adaptive filters for feature extraction,and a random forest(RF)classifier is thus adopted for classification.The effectiveness of our ShearSAF approach is verified on three real-world datasets,and the results show that the performance of ShearSAF can achieve an accuracy higher than that of comparison methods when exploiting small-size training sample problems.The codes of this work will be available at http://jiasen.tech/papers/for the sake of reproducibility.

关键词： lidar Shear utilize

First time observation of sodium layer over Wuhan,China by sodium fluorescence lidar

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Science China Mathematics 1997年第11期40卷 1228-1232页

作者：龚顺生曾锡之薛新建郑文钢胡志林贾汉春张海枝刘谊平 Wuhan Institute of Physics Chinese Academy of SciencesWuhan 430071China

The structure of the WIP sodium fluorescence lidar and the preliminary observation result for the sodium layer are reported.

关键词： sodium layer lidar resonance fluorescence

Autonomous exploration using UWB and lidar

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Journal of Automation and Intelligence 2023年第1期2卷 51-60页

作者： Mingyang Guan Changyun Wen Advanced Remanufacturing and Technology Centre A*STARSingapore School of Electrical and Engineering Nanyang Technological UniversitySingapore

In autonomous exploration,a robot navigates itself in an unknown environment while building a 2D map of the environment.This is typically done using a lidar sensor,which however is susceptible to error accumulation.To handle this issue,a UWB/lidar fusion SLAM is proposed,which can be decoupled into a localization problem and a mapping problem.For localization problem,we firstly apply extended Kalman filter(EKF)to localize all UWB beacons and then use particle filter(PF)to estimate the robot’s state based on the two on-board UWB nodes’estimated locations.For mapping problem,we firstly fine-tune the robot’s state using a recursive adaptive-trust-region scan matcher,which is termed as RASM,and then construct the map based on the refined robot’s state.We also propose a method to correct UWB beacons’locations using the robot’s refined location.Furthermore,the information obtained from the proposed fusion SLAM is utilized to sketch the region where the robot is going to explore next.That is,a where-to-explore strategy is proposed to guide the robot to the less-explored areas.Overall,the proposed exploration system is infrastructure-less and avoid mapping error to accumulate over time.Extensive experiments with comparisons to the state-of-the-art methods are conducted in two different environments:a cluttered workshop and a spacious garden in order to verify the effectiveness of our proposed strategy.The experimental tests are filmed and the video is available in the supplementary materials.

关键词： lidar refined error