Detection,segmentation and classification of 3D urban objects using mathematical morphology and supervised learning     

《ISPRS Journal of Photogrammetry and Remote Sensing》2014

We propose an automatic and robust approach to detect, segment and classify urban objects from 3D point clouds. Processing is carried out using elevation images and the result is reprojected onto the 3D point cloud. First, the ground is segmented and objects are detected as discontinuities on the ground. Then, connected objects are segmented using a watershed approach. Finally, objects are classified using SVM with geometrical and contextual features. Our methodology is evaluated on databases from Ohio (USA) and Paris (France). In the former, our method detects 98% of the objects, 78% of them are correctly segmented and 82% of the well-segmented objects are correctly classified. In the latter, our method leads to an improvement of about 15% on the classification step with respect to previous works. Quantitative results prove that our method not only provides a good performance but is also faster than other works reported in the literature.  相似文献   

基于街景影像多特征融合的广州市越秀区街道空间品质评估     

崔成  任红艳  赵璐  庄大方 《地球信息科学学报》2020,22(6):1330-1338

全面准确地描述街景影像的多层次特征在基于街景影像对街道空间品质进行评估的研究中具有重要意义。以广州市越秀区为例,获取前后左右各视角街景影像中手工设计的特征（SURF特征、HOG特征）和基于深度学习的特征（语义特征）,基于单一特征和多特征融合采用支持向量机（Support Vector Machine, SVM）、随机森林（Random Forest, RF）训练各视角的评估模型。结果表明,以基于SVM建立的单特征模型为例,基于HOG特征（73.03%）、语义特征（72.28%）的模型平均精度优于SURF特征（56.00%）,基于SVM前后左右各视角模型的最优分类精度为82.8%（前）、81.7%（后）、76.6%（左）、76.6%（右）,而基于RF各视角模型的最优分类精度为82.8%（前）、85.0%（后）、78.1%（左）、70.3%（右）。前后视角的模型精度略优于左右视角。各视角最优模型均为多特征融合模型,最优模型平均分类精度和Kappa系数可达80.6%和0.62。利用街景影像评估街道空间品质时,各算法之间性能差异微弱,而特征选择及组合方式是提升精度的关键。越秀区街道空间品质存在明显空间分异,其西南部的街道空间品质亟待提升。本研究构建了基于街景影像多特征融合的大规模高精度街道空间品质测度方法,实现了对越秀区街道空间品质的评估,研究结果可为相关部门进行街道环境综合整治提供参考。  相似文献   

基于EM算法优化相关向量机的BDS-3超快速钟差预报     

胡燕  王德盛  杨玉锋 《大地测量与地球动力学》2021,41(12):1230-1234

提出一种基于EM算法优化相关向量机(RVM)的BDS-3超快速钟差预报算法。首先,利用组合MAD法预处理钟差数据,并进行一次差分计算;然后,利用钟差一次差分数据对RVM模型进行训练,通过EM算法迭代求取模型的超参数;最后,利用优化后的RVM模型进行数据预测,将钟差一次差分预测值还原,得到钟差预报值。采用iGMAS中心提供的实测BDS-3超快速钟差数据进行预报实验,并将本文模型与QP模型、SA模型及iGMAS超快速钟差预报产品(ISU-P)结果进行对比分析。结果表明,对于6 h、12 h和24 h预报,本文模型预报BDS-3卫星钟差数据的平均精度均优于0.61 ns;与ISU-P、QP模型和SA模型相比,本文模型预报24 h时精度分别提升64.1%、50.0%和49.2%。  相似文献   

岩爆分类的支持向量机方法   总被引：14，自引：0，他引：14  

赵洪波 《岩土力学》2005,26(4):642-644

针对岩爆分类问题,提出了基于支持向量机的分类方法。通过对影响岩爆因素的分析,运用支持向量机理论建立岩爆类别的支持向量机模型。结果表明,基于支持向量机的岩爆分类方法具有较高的准确率,该方法是科学可行的,具有广泛的应用前景。  相似文献   

TBM施工隧道仰拱预制块的力学特性研究   总被引：2，自引：0，他引：2  

周佳媚  高波  李志业 《岩土力学》2004,25(12):1973-1976

针对TBM施工隧道仰拱预制块的特殊结构,采用围岩-结构模式,进行了模拟施工动态的受力影响计算分析。计算结果表明,围岩初始应力场对仰拱受力有明显影响, 随着竖向压力的增大,仰拱中心的内、外侧应力均增大;随着水平压力的增大,仰拱中心内侧应力减小、外侧应力基本不变;减小复合衬砌结构中初期支护厚度以及变刚性接头为柔性接头均会改善仰拱受力。最后,用试验数据验证计算的正确性,其结论可供TBM施工隧道仰拱预制块的优化设计参考。  相似文献   

填土压实质量检测及机载压实集成系统分析     

范云  汪英珍 《岩土力学》2004,25(4):605-608

对既有及正在开发中的填土压实检测技术的原理及实测方法进行了系统的介绍、分析及论证。在此基础上,阐述了开发填土压实检测与压实机械集成系统的必要性、研制机载集成系统时所依据的力学模型、实现途径及国内外关于压实机械集成系统研究、开发的最新进展,介绍了压实机械集成系统的工作原理及工作特性,同时指出了为适应当前及未来土建工程对填土压实质量（特别是沉降量及沉降速率等方面）的基本要求,应以填土压实层的力学强度（K30、动刚度系数等）作为开发压实机械集成系统时的参考对比指标。  相似文献   

铣刨机在高速公路养护维修工程中的应用     

付建伟 《煤田地质与勘探》2001,29(2):60-61

近年来高等级公路正在我国迅速发展,养护与维修路面是一项迫切任务。以2000 DC型铣刨机为例介绍其在维修路面中的作用,以及这种铣刨机的结构、技术参数、主要性能及现场应用注意事项。   相似文献   

Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models     

Huihui Zhang  Hugo A. Loáiciga  Fu Ren  Da Ha 《水文科学杂志》2020,65(11):1928-1942

ABSTRACT

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.  相似文献   

Machine learning based identification of dominant controls on runoff dynamics     

Henning Oppel  Andreas H. Schumann 《水文研究》2020,34(11):2450-2465

Hydrological models used for flood prediction in ungauged catchments are commonly fitted to regionally transferred data. The key issue of this procedure is to identify hydrologically similar catchments. Therefore, the dominant controls for the process of interest have to be known. In this study, we applied a new machine learning based approach to identify the catchment characteristics that can be used to identify the active processes controlling runoff dynamics. A random forest (RF) regressor has been trained to estimate the drainage velocity parameters of a geomorphologic instantaneous unit hydrograph (GIUH) in ungauged catchments, based on regionally available data. We analyzed the learning procedure of the algorithm and identified preferred donor catchments for each ungauged catchment. Based on the obtained machine learning results from catchment grouping, a classification scheme for drainage network characteristics has been derived. This classification scheme has been applied in a flood forecasting case study. The results demonstrate that the RF could be trained properly with the selected donor catchments to successfully estimate the required GIUH parameters. Moreover, our results showed that drainage network characteristics can be used to identify the influence of geomorphological dispersion on the dynamics of catchment response.  相似文献   

Development of a real-time forecasting model for turbidity current arrival time to improve reservoir desilting operation     

Ming-Jui Chang  Peng-An Chen  Fong-Zuo Lee  Jihn-Sung Lai 《水文科学杂志》2020,65(6):1022-1035

ABSTRACT

Among various strategies for sediment reduction, venting turbidity currents through dam outlets can be an efficient way to reduce suspended sediment deposition. The accuracy of turbidity current arrival time forecasts is crucial for the operation of reservoir desiltation. A turbidity current arrival time (TCAT) model is proposed. A multi-objective genetic algorithm (MOGA), a support vector machine (SVM) and a two-stage forecasting technique are integrated to obtain more effective long lead-time forecasts of inflow discharge and inflow sediment concentration. The multi-objective genetic algorithm (MOGA) is applied for determining the optimal inputs of the forecasting model, support vector machine (SVM). The two-stage forecasting technique is implemented by adding the forecasted values to candidate inputs for improving the long lead-time forecasting. Then, the turbidity current arrival time from the inflow boundary to the reservoir outlet is calculated. To demonstrate the effectiveness of the TCAT model, it is applied to Shihmen Reservoir in northern Taiwan. The results confirm that the TCAT model forecasts are in good agreement with the observed data. The proposed TCAT model can provide useful information for reservoir sedimentation management during desilting operations.  相似文献