Aiming at the lack of detailed comparison of existing airborne LiDAR point cloud filtering algo-rithms,a surface subsidence area in a western mining area was selected as the experimental area.Three classic algo-rithms,namely progressive morphological filtering,triangular network progressive encryption filtering,and slope threshold filtering,were selected for denoising,and the applicability of the algorithms was analyzed.The experimental results show that in the mining area of northern Shaanxi,where the surface fluctuation is obvious and the vegetation is sparse,the point cloud modeling effect after denoising is the best by using triangulation progressive morphological filtering.The test results can provide important technical means for fine modeling of mining subsidence in mining ar-eas.
Indoor navigation, indoor robotics, and other deep applications of interior space can be realized through semantic segmentation of 3D point clouds. We propose a semantic segmentation method for point clouds that uses geometric features of point clouds and neural networks to address the problem of incomplete and inconsistent segmentation objectives in existing semantic segmentation methods. Using neural networks, semantic labels are extracted from indoor structural information as the first step. The paper proposes a probabilistic model to cross-validate the initial segmentation results with the segmentation results of geometric features to achieve joint optimization of the results for semantic segmentation. Three sets of indoor point clouds data from simple to complex indoor scenes are used to test the accuracy and validity of the segmentation method proposed in this paper. The experimental results demonstrate that the method proposed in this paper can effectively improve the semantic segmentation accuracy of indoor 3D point clouds.
阴影是山区高分航空影像严重的干扰因素,去除山体阴影有助于提高实景三维建设、林业调查、变化检测等应用的准确性和有效性.本文构建了高分航空遥感影像的山体阴影指数(MSI),并提出了基于色彩迁移和色彩均衡的阴影去除方法.采用覆盖山区的0.2 m航空影像进行试验,结果表明,MSI和阈值分割法可以有效地检测航空影像的山体阴影,而结合对象化色彩迁移和基于邻域的非线性色彩均衡的阴影去除法既能有效地消除山体阴影,又能使阴影区域恢复纹理细节,色彩更贴合于非阴影区域,达到影像整体色彩更加均衡的效果.分析阴影去除前后的统计指标发现,随着阴影的去除,各波段平均值和标准差都明显增加,说明阴影区域的亮度有提高且色彩层次更加丰富.
结合Sentinel-2影像及其他高分辨率卫星数据进行长序列、高频次、大范围的水面率、蓄水量、生态流量等水资源要素监测具有重要意义.为了提高水体提取精度,解决利用多源中高分辨率卫星数据提取水体时的空间尺度效应问题,本文提出了一种面向Sentinel-2影像的亚像元级水体提取方法(简称SWES).首先利用RWI提取纯水体像元,然后利用膨胀算法提取水陆边界混合像元,最后为解决地物的类内光谱变化问题,采用考虑空间信息的多端元光谱混合分析算法(MESMA)求解水陆混合像元中的水体丰度.3个试验区的结果均表明,SWES取得了较好效果,平均RMSE为0.147,水体提取效果均优于自动亚像元水体提取方法(简称ASWM),尤其在水陆混合像元较多的坑塘养殖区.SWES在试验区获取的水体面积也有较高精度,平均相对误差为8.03％,低于ASWM的20.23％,结果表明SWES能够有效提升水域面积提取精度.
为了满足停车场快速建图的需求,本文提出了基于手持激光点云的室内停车场地面标识要素的提取方法.首先,为减少要素提取对内存空间的需求,将整个点云以规则网格进行划分;其次采用RANSAC平面拟合的方法提取每个网格内的地面点云;然后为提取地面标识要素,根据地面点云生成地面图像,并在地面图像的基础上,采用BiSeNet网络对不同的标识要素进行语义分割,得到车道线、车位线和导向箭头标识的像素;最后针对车道线和车位线,采用基于霍夫变换的直线提取方法对其进行提取,对于地面导线箭头,采用模板匹配的方法对其进行提取.试验证明,本文提出的方法能够对扫描的结构要素和标识要素进行快速提取,可大大减少制图的人工工作量,有效提高室内停车场建图的效率.
构建能与现实场景互联互通的实景三维数字基底,可为加快构建自然资源统一监测体系提供高质量的监测成果及地理信息服务.本文在自然资源统一调查监测评价框架下,开展自然资源季度监测工作,研究基于实景三维的自然资源调查监测实际应用.以常熟市自然资源监测省级试点为例,利用实景三维技术手段,为常熟市自然资源保护与管理提供技术支撑.结果表明,多尺度、时序化、关联化的实景三维技术表现力强、分析结果精准、监测效果优,能高质量地支撑自然资源监测应用场景.
为提高土壤湿度反演精度,并克服基于单一卫星、单一频点开展土壤湿度反演存在的不足,本文提出了一种结合机器学习的GNSS-IR多卫星双频组合土壤湿度反演方法,将GNSS卫星L1、L2频点上SNR作为数据源进行土壤湿度反演研究,采用BP和RBF神经网络算法构建土壤湿度预测模型,并与一元线性回归预测模型进行对比分析.试验结果表明:①相对于单卫星而言,多卫星组合的土壤湿度反演增加了有效卫星利用率,并提高了土壤湿度反演的精度;②多星组合的L1、L2双频均值融合延迟相位观测值与土壤湿度的相关系数为0.956,均优于L1、L2频点反演结果;③BP、RBF神经网络模型预测结果精度均优于ULR模型预测结果.
对月表不同尺寸撞击坑的提取具有重要研究价值.目前针对直径1 km以下的撞击坑检测取得了理想的效果,但对于相对较大的撞击坑检测率有待进一步提升.本文提出了一种具有良好稳健性的撞击坑自动检测模型,基于LOLA发布的全月DEM数据生成了月表地形参数,采用面向对象的多层次分割方法并结合机器学习技术提取撞击坑,选取3个典型样区进行了试验分析.结果表明,对于直径范围在1～120 km内的撞击坑,召回率和精确率分别为86.5％和81.2％,具有良好的检测率.
A curve enhanced lane detection algorithm based on cyclic feature fusion Resa-CC is proposed to address the issue of reduced accuracy in curve recognition caused by excessive curvature at road turns. This algorithm utilizes the shape priors of lane lines to capture the spatial relationships between rows and columns in image pixels, and fuse information to generate feature maps. The residual network is used as the main framework, and the encoder, decoder and attention mechanism modules are added. The Loss function adds curve structure constraints to improve the recognition accuracy of lane curves. The addition of the cyclic feature fusion module and the self attention mechanism module improved the accuracy by 3.41% and 1.1%, respectively, proving the effectiveness of the two modules. The accuracy of the Resa-CC algorithm can reach 96.83%, with an FPS of 35.68. The false detection rate FP and missed detection rate FN are 0.0315 and 0.0282, respectively. This indicates that the algorithm has high detection performance and can more accurately infer the position of the lane line in the curve when vehicles are driving.
车载激光扫描技术在进行作业时需要架设基站进行后处理差分解算,且只能在基站一定范围内进行采集,局限性很大.针对这一情况,本文提出了一种免基站扫描技术,采用加装RTK模块,利用轨迹固定解点位对单点定位轨迹进行后处理修正,很好地解决了精度问题.此方式免去了架设基站产生的人力时间成本,扫描效率提高了50％,人力成本减少了一半,且可自由规划扫描,进一步优化了车载移动扫描用于质检工作的方案,提高了质检效率.试验结果表明,该方案精度与架设基站方案相当,扫描效率大大提高.
对标新工科发展理念,教学团队立足于"GIS空间分析"的学科交叉特点,针对过往教学痛点,探索面向国际学术前沿和行业最新需求的地理信息科学专业授课模式,提出了兼顾实践与创新能力的多元化情境-角色-任务(DSRT)教学模式.通过知识体系重构、综合培养导向的情境活动设计、四维兼顾的考核评价体系构建,激发学生学习兴趣,全面培养学生的地学素养、工程实践和创新能力,有效提升学生解决复杂问题的综合能力,为高校复合型地理信息科学人才培养提供参考.
In order to cope with the changing road environment during driving and divide the drivable area of the current road in front of the vehicle, this paper proposes a detection method for the road in front of the vehicle based on multi-feature fusion difference. This algorithm extracts the ground point cloud from the original point cloud by morphological filtering method, statistically summarizes the ground point cloud data to define the operation domain, divides the differential element size and starting point of different depths in the operation domain, fuses the characteristic parameters in the differential element, forms a feature matrix, solves the differential matrix, and performs threshold filtering, so as to realize the extraction of the point cloud in front of the vehicle. In this paper the extraction algorithm of the relevant road point cloud is compared to,which highlight its excellent performance and then the road extraction effect of different depths of the collected data is compared to prove the effectiveness of the algorithm.
针对复杂异形古建筑的实景三维建模问题,本文提出了一种基于运动恢复结构(SfM)的低成本、简单、高效的视频帧影像三维重构方法.首先,选用单镜头消费级无人机,通过设计规划合理的飞行路径,获取高分辨率的视频帧影像,并利用SfM算法生成细节丰富的复杂异形古建筑三维模型.以宁夏银川市兴庆区明代鼓楼为例,从建模效率和建模质量两个方面对3款平台进行量化差异性分析,探求最优平台使用策略.结果表明:该三维重构方式能够高效实现复杂异形古建筑的表面精细纹理获取与实景三维模型重构,其中ContextCapture平台建立模型效果优于PhotoScan、RealityCapture平台,建模质量和纹理清晰度均表现较佳,建议首选使用ContextCapture平台实现三维重构.本文方法可为复杂异形古建保护的三维重构策略提供依据,具有广泛的应用前景.
大地测量基准作为现代基础测绘的重要组成部分,其建立和维持的技术手段、工具及理论方法随着社会的发展及科学的进步发生了巨大的变化.上海市卫星导航定位基准服务系统(SHCORS)通过格网化VRS技术,采用"发布七参数"的作业模式,实现了面向海量用户服务,为上海地区提供了高精度、稳定的位置服务.本文对SHCORS系统构建中的关键技术进行了探讨与研究,并对其实时定位精度进行了测试.结果表明,SHCORS系统的精度满足实际应用需求.
湖泊是组成生态系统的重要结构之一,探究黄河源头鄂陵湖面积变化,分析其驱动因素,可为黄河流域生态保护和环境治理提供理论依据和技术支持.本文选取2001—2020年Landsat系列遥感影像,结合气象、植被覆盖、土地利用类型等因素,采用NDWI、Mann-Kendall检验和Pearson相关性分析等方法,对鄂陵湖面积变化及其驱动力进行分析研究.结果表明:2001—2020年鄂陵湖平均面积为639.10 km2,最大值出现在2012年,为676.28 km2,最小值出现在2001年,为595.92 km2,湖泊面积变化经历稳定期(2001—2004年)、扩张期(2004—2012年)、收缩期(2012—2017年)、扩张期(2017—2020年)4个阶段,整体呈现扩张趋势;空间上,近20年鄂陵湖西南部区域面积变化最为明显,2018年较上年面积扩张20.41 km2,湖泊收缩期西南部区域湖泊边界呈现出持续收缩的趋势,基础地质条件及气象条件的变化是西南部区域面积变化的主要原因;按各因子与湖泊面积的相关性大小,2001—2020年鄂陵湖面积变化的驱动因素从主到次依次为:降水、气温、植被覆盖、蒸发量、土地利用类型,降雨和气温是影响鄂陵湖面积变化的主要因素.
Due to the mining history of Datong coal field for many years, it has destroyed the agriculture, forestry and grassland, and become a fragile ecological environment area, resulting in the change of carbon cycle in the coal field area. With the green development in recent years, the ecological environment has improved, and the carbon sink has increased. Taking Datong coal field as the research area, based on the hyperspectral remote sensing image data of Zhuhai No.1 in 2020 and 2021, this paper studies the carbon sink change in the coal field area in the two years from the perspective of land use change. The conclusions are as follows: ①the land use structure of Datong coal field area has not changed significantly in the two years, and the grassland, forest land and cultivated land have increased by 2.57 km2, 0.71 km2 and 0.11 km2 respectively; ②the carbon sink of Datong coal field in 2021 has increased by 30 000 t CO2 compared with that in 2020. The ecological environment of the coal field has gradually improved.
To solve the problem of difficult and slow real-time automated detection of defects in water supply pipelines, a new intelligent identification and positioning method for water supply pipelines is proposed based on a dataset of pipeline defect data collected from actual engineering projects. The new YOLOX algorithm model, which incorporates an attention module, is developed and used for algorithm training and prediction using a dataset of video frames. Test results show that the YOLOX algorithm model with attention mechanism achieved an average testing accuracy of 94%, a mAP value of 84%, and an average recognition speed of 16 m/s. Additionally, compared with three other commonly used algorithm models (YOLO V3 and Fast R-CNN), the new model showed the best overall performance. This proposed model can also be applied to real-time video detection, providing an efficient and accurate detection technology and method for the intelligent identification and positioning of defects in water supply pipelines.
针对基于位置指纹的Wi-Fi室内定位方法定位精度低的问题,本文提出了一种融合卷积神经网络(CNN)和胶囊网络(CapsNet)的Wi-Fi室内定位算法模型,记为CNN-CapsNet.首先将采集的RSSI时间序列信息,生成位置指纹图像数据集;然后通过由卷积层和池化层构成的CNN初级特征提取器,完成定位图像到初级特征图的转换;最后将初级特征图输入到CapsNet中,获得最终的分类结果.试验结果表明,在不同的向量维度,迭代次数等参数下,该模型的准确率高达99.99％,损失函数值低至0.00991,优于其他的传统定位方法.
构建生态空间调控网络对于维持生态空间本底基础具有重要意义.本文以甘肃省景泰县为例,运用生态系统服务价值评估模型和生态环境敏感性评估模型识别生态源地,通过使用最小累计阻力模型(MCR)和重力模型,建立阻力面,提取生态廊道和生态节点,并以此构建生态空间调控网络.结果表明,研究区内有21处生态源地呈四周均匀分布、中部分散稀疏的空间分布特征,共识别出41条生态廊道和43处生态节点.结合关键区域的不同地理特征和地类现状,提出自然保护为主、人为修复为辅,以及人为修复与自然保护并重的两类修复策略.研究结果可识别生态修复区与构建区域生态空间调控网络.
Ancient tombs are precious heritage of the development of human civilization, and they are usually with extremely high historical, cultural and artistic values. Affected by various natural and man-made factors, ancient tombs are often destroyed to some extent, therefore, the digital protection of ancient tombs are urgently needed. It is usually difficult to collect the underground data by traditional tools such as total station and RTK given that ancient tombs are generally hidden underground. To this end, this paper uses the Trimble X12 3D laser scanner to realize the accurate collection of point cloud data of ancient tombs. Then, the internal and external real scenes of the ancient tombs are truly restored through 3D modeling, and on this basis, the digital protection application of the ancient tombs is studied. The research results show that this technology can provide a solid data foundation for the restoration and protection of ancient tombs.
