基于机载激光雷达LiDAR(Light Detection and Ranging)数据识别震后建筑物震害,其前提是快速准确地提取建筑物点云。通过分析地震灾区机载激光雷达点云中提取建筑物点云的诸多难点,已有的方法难以达到预期效果,因此提出融合同机航空影...基于机载激光雷达LiDAR(Light Detection and Ranging)数据识别震后建筑物震害,其前提是快速准确地提取建筑物点云。通过分析地震灾区机载激光雷达点云中提取建筑物点云的诸多难点,已有的方法难以达到预期效果,因此提出融合同机航空影像数据的方法,实现了震后灾区建筑物点云的获取。该方法首先在数据预处理的基础上,利用布料模拟滤波CSF(Cloth Simulation Filtering)算法进行点云滤波,得到地面点云和非地面点云(主要是建筑物、植被和车辆行人等),并将航空影像红波段光谱信息赋予非地面点云;然后基于灰度直方图阈值分割的方法剔除植被点;最后对剩余激光脚点利用具有噪声的基于密度的空间聚类DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法进行聚类提取最终的建筑物点,并与参考建筑物点比对,进行精度验证,得到建筑物点云提取的漏检概率、虚警概率分别为15.61%、7.52%,总体精度可达84.39%。结果表明,在一定精度要求范围内,该方法能有效实现地震灾区建筑物点云的提取,可为震后机载LiDAR建筑物点云提取提供技术参考和方法借鉴,为建筑物震害识别做好基础工作。展开更多
航拍影像富含光谱信息、纹理信息和空间信息,机载LiDAR(Light Detection and Ranging)能够提供地物的三维信息。综合利用两类数据的优势,研究了一种面向对象的城市地物分类方法。通过预处理将LiDAR点云转换成二维栅格数据,与航拍影像进...航拍影像富含光谱信息、纹理信息和空间信息,机载LiDAR(Light Detection and Ranging)能够提供地物的三维信息。综合利用两类数据的优势,研究了一种面向对象的城市地物分类方法。通过预处理将LiDAR点云转换成二维栅格数据,与航拍影像进行配准;结合光谱信息和高度信息对研究影像进行多尺度分割,依据最优分割尺度计算模型选择最优分割尺度;对分割对象提取各类特征,采用XGBoost算法进行特征选择,选择支持向量机(Support Vector Machine,SVM)分类器进行分类,为体现XGBoost算法的优势,借助SVM分类器与Relief和RFE两种传统的特征选择算法比较;基于一定规则将阴影区域地物区分以及合并到真实地物类别中,实现最终的城市地物分类。在3个区域测试分类方法,结果表明本文研究方法可行有效,能够较好地应用于城市地物分类。展开更多
The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes.However,accurate and fast semantic segmentation of highresolution aerial images for remote ...The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes.However,accurate and fast semantic segmentation of highresolution aerial images for remote sensing applications is still facing three challenges:the requirements for limited processing resources and low-latency operations based on aerial platforms,the balance between high accuracy and real-time efficiency for model performance,and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images.To address these issues,a lightweight and dual-path deep convolutional architecture,namely Aerial Bilateral Segmentation Network(Aerial-Bi Se Net),is proposed to perform realtime segmentation on high-resolution aerial images with favorable accuracy.Specifically,inspired by the receptive field concept in human visual systems,Receptive Field Module(RFM)is proposed to encode rich multi-scale contextual information.Based on channel attention mechanism,two novel modules,called Feature Attention Module(FAM)and Channel Attention based Feature Fusion Module(CAFFM)respectively,are proposed to refine and combine features effectively to boost the model performance.Aerial-Bi Se Net is evaluated on the Potsdam and Vaihingen datasets,where leading performance is reported compared with other state-of-the-art models,in terms of both accuracy and efficiency.展开更多
Slope failures are an inevitable aspect of economic pit slope designs in the mining industry.Large open pit guidelines and industry standards accept up to 30%of benches in open pits to collapse provided that they are ...Slope failures are an inevitable aspect of economic pit slope designs in the mining industry.Large open pit guidelines and industry standards accept up to 30%of benches in open pits to collapse provided that they are controlled and that no personnel are at risk.Rigorous ground control measures including real time monitoring systems at TARP(trigger-action-response-plan)protocols are widely utilized to prevent personnel from being exposed to slope failure risks.Technology and computing capability are rapidly evolving.Aerial photogrammetry techniques using UAV(unmanned aerial vehicle)enable geotechnical engineers and engineering geologists to work faster and more safely by removing themselves from potential line-of-fire near unstable slopes.Slope stability modelling software using limit equilibrium(LE)and finite element(FE)methods in three dimensions(3D)is also becoming more accessible,user-friendly and faster to operate.These key components enable geotechnical engineers to undertake site investigations,develop geotechnical models and assess slope stability faster and in more detail with less exposure to fall of ground hazards in the field.This paper describes the rapid and robust process utilized at BHP Limited for appraising a slope failure at an iron ore mine site in the Pilbara region of Western Australia using a combination of UAV photogrammetry and 3D slope stability models in less than a shift(i.e.less than 12 h).展开更多
基于深度学习的目标检测算法是目前目标检测领域最流行的算法,但是由于硬件条件的限制,算法输入图像的尺寸受到限制。对于大尺寸的航拍图像,通常先采用滑窗法提取区域,再对提取的区域进行检测,极大地降低了算法的检测速度。针对这一问题...基于深度学习的目标检测算法是目前目标检测领域最流行的算法,但是由于硬件条件的限制,算法输入图像的尺寸受到限制。对于大尺寸的航拍图像,通常先采用滑窗法提取区域,再对提取的区域进行检测,极大地降低了算法的检测速度。针对这一问题,本文根据航拍图像中人造物体含有大量边缘的特点,提出了一种基于深度学习的梯度聚类目标检测算法,并阐述了其模型结构与工作原理,然后通过151张航拍图像数据集测试,对比评估了梯度聚类SSD方法与滑窗SSD方法在航拍图像检测上的检测精度和检测速度。结果表明:梯度聚类SSD方法的FPS(Frames Per Second)为0.499,SPF(Seconds Per Frame)为2.00,mAP(mean Average Precision)为46.93,相比滑窗SSD方法,在损失11.72%的检测精度的条件下,FPS提高了64.69%(SPF提高了40.40%),验证了所提出算法的有效性。展开更多
数学精度、要素属性正确性、逻辑一致性是当前地理信息产品质量检测的主要内容,其中精度和属性的检查多采用人工外业实地巡检方式,检测数据的获取具有离散性,属性正确性检查受人为因素影响较大,且该方法劳动强度大、成本高、效率低,对...数学精度、要素属性正确性、逻辑一致性是当前地理信息产品质量检测的主要内容,其中精度和属性的检查多采用人工外业实地巡检方式,检测数据的获取具有离散性,属性正确性检查受人为因素影响较大,且该方法劳动强度大、成本高、效率低,对于特殊区域的检查实施困难。通过研究面向地理信息产品质量检测的低空机载激光点云、影像、视频及位置与姿态数据(position and orientation system,POS)的融合处理技术,提出了基于多源低空遥感数据的地理信息产品要素数学精度分类检测与属性评估方法。研究表明,本文方法能够有效用于地理信息产品的质量检测。展开更多
文摘基于机载激光雷达LiDAR(Light Detection and Ranging)数据识别震后建筑物震害,其前提是快速准确地提取建筑物点云。通过分析地震灾区机载激光雷达点云中提取建筑物点云的诸多难点,已有的方法难以达到预期效果,因此提出融合同机航空影像数据的方法,实现了震后灾区建筑物点云的获取。该方法首先在数据预处理的基础上,利用布料模拟滤波CSF(Cloth Simulation Filtering)算法进行点云滤波,得到地面点云和非地面点云(主要是建筑物、植被和车辆行人等),并将航空影像红波段光谱信息赋予非地面点云;然后基于灰度直方图阈值分割的方法剔除植被点;最后对剩余激光脚点利用具有噪声的基于密度的空间聚类DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法进行聚类提取最终的建筑物点,并与参考建筑物点比对,进行精度验证,得到建筑物点云提取的漏检概率、虚警概率分别为15.61%、7.52%,总体精度可达84.39%。结果表明,在一定精度要求范围内,该方法能有效实现地震灾区建筑物点云的提取,可为震后机载LiDAR建筑物点云提取提供技术参考和方法借鉴,为建筑物震害识别做好基础工作。
基金co-supported by the National Natural Science Foundation of China(Nos.U1833117 and 61806015)the National Key Research and Development Program of China(No.2017YFB0503402)。
文摘The aircraft system has recently gained its reputation as a reliable and efficient tool for sensing and parsing aerial scenes.However,accurate and fast semantic segmentation of highresolution aerial images for remote sensing applications is still facing three challenges:the requirements for limited processing resources and low-latency operations based on aerial platforms,the balance between high accuracy and real-time efficiency for model performance,and the confusing objects with large intra-class variations and small inter-class differences in high-resolution aerial images.To address these issues,a lightweight and dual-path deep convolutional architecture,namely Aerial Bilateral Segmentation Network(Aerial-Bi Se Net),is proposed to perform realtime segmentation on high-resolution aerial images with favorable accuracy.Specifically,inspired by the receptive field concept in human visual systems,Receptive Field Module(RFM)is proposed to encode rich multi-scale contextual information.Based on channel attention mechanism,two novel modules,called Feature Attention Module(FAM)and Channel Attention based Feature Fusion Module(CAFFM)respectively,are proposed to refine and combine features effectively to boost the model performance.Aerial-Bi Se Net is evaluated on the Potsdam and Vaihingen datasets,where leading performance is reported compared with other state-of-the-art models,in terms of both accuracy and efficiency.
文摘Slope failures are an inevitable aspect of economic pit slope designs in the mining industry.Large open pit guidelines and industry standards accept up to 30%of benches in open pits to collapse provided that they are controlled and that no personnel are at risk.Rigorous ground control measures including real time monitoring systems at TARP(trigger-action-response-plan)protocols are widely utilized to prevent personnel from being exposed to slope failure risks.Technology and computing capability are rapidly evolving.Aerial photogrammetry techniques using UAV(unmanned aerial vehicle)enable geotechnical engineers and engineering geologists to work faster and more safely by removing themselves from potential line-of-fire near unstable slopes.Slope stability modelling software using limit equilibrium(LE)and finite element(FE)methods in three dimensions(3D)is also becoming more accessible,user-friendly and faster to operate.These key components enable geotechnical engineers to undertake site investigations,develop geotechnical models and assess slope stability faster and in more detail with less exposure to fall of ground hazards in the field.This paper describes the rapid and robust process utilized at BHP Limited for appraising a slope failure at an iron ore mine site in the Pilbara region of Western Australia using a combination of UAV photogrammetry and 3D slope stability models in less than a shift(i.e.less than 12 h).
文摘基于深度学习的目标检测算法是目前目标检测领域最流行的算法,但是由于硬件条件的限制,算法输入图像的尺寸受到限制。对于大尺寸的航拍图像,通常先采用滑窗法提取区域,再对提取的区域进行检测,极大地降低了算法的检测速度。针对这一问题,本文根据航拍图像中人造物体含有大量边缘的特点,提出了一种基于深度学习的梯度聚类目标检测算法,并阐述了其模型结构与工作原理,然后通过151张航拍图像数据集测试,对比评估了梯度聚类SSD方法与滑窗SSD方法在航拍图像检测上的检测精度和检测速度。结果表明:梯度聚类SSD方法的FPS(Frames Per Second)为0.499,SPF(Seconds Per Frame)为2.00,mAP(mean Average Precision)为46.93,相比滑窗SSD方法,在损失11.72%的检测精度的条件下,FPS提高了64.69%(SPF提高了40.40%),验证了所提出算法的有效性。
文摘数学精度、要素属性正确性、逻辑一致性是当前地理信息产品质量检测的主要内容,其中精度和属性的检查多采用人工外业实地巡检方式,检测数据的获取具有离散性,属性正确性检查受人为因素影响较大,且该方法劳动强度大、成本高、效率低,对于特殊区域的检查实施困难。通过研究面向地理信息产品质量检测的低空机载激光点云、影像、视频及位置与姿态数据(position and orientation system,POS)的融合处理技术,提出了基于多源低空遥感数据的地理信息产品要素数学精度分类检测与属性评估方法。研究表明,本文方法能够有效用于地理信息产品的质量检测。
