粗粒混合钠盐盐渍土的地质雷达探测适用性研究

为了研究粗粒混合钠盐盐渍土的地质雷达探测适用性,在室内制作了2×1.0×1.5m的人工模型箱,通过人工配制的方式调配了4组具有不同含水率和含盐量的模型填土;采用900MHz的屏蔽式天线对模型填土进行探测并由此获得相应的探测图像,通过图像特征对探测适用性进行初步判定;为了对初步判定进行验证,以西安市杨凌北部黄土台塬区为实验场地进行现场探测,综合分析表明：含水率是影响探测适用性的主要因素,当含水率大于35%时,不论含盐量多少,探测图像模糊不清,均具有“斑点状”特征,难以进行有效判读,且回波主频集中,主频不超过中心频率的1/3,低频特征明显;当含水率低于20%时,含盐量的增加会显著提高电磁反射的强度,探测图像反射明显,图像清晰可见,可判性良好。研究内容能为粗粒混合钠盐盐渍土的地质雷达现场探测提供相关参考。     

Maintaining accurate,current, rural road network data: An extraction and updating routine using RapidEye,participatory GIS and deep learning

Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained.     

完全残差连接与多尺度特征融合遥感图像分割

遥感图像数据规模大,光照、遮挡等情况复杂,目标密集、尺度不一以及缺乏大量带标注图像用于训练深度网络等特点对遥感图像分割的完整性和正确性造成了更大的挑战。针对深度卷积网络中因多次卷积造成分辨率显著下降,像素类别预测精度降低的问题,本文在深度卷积编码—解码网络的基础上设计了一个采用完全残差连接和多尺度特征融合的端到端遥感图像分割模型。该模型具有两方面优点：首先,长距离和短距离的完全残差连接既简化了深层网络的训练,又为本层末端融入了原始输入信息,增强了特征融合。其次,不同尺度和方式的特征融合使网络能够提取丰富的上下文信息,应对目标尺度变化,提升分割性能。本文通过对ISPRS Vaihingen和Road Detection数据集做数据扩充并进行实验,分别从平均IOU、平均F1值两方面对模型进行评价。通过与目前先进的模型以及文献中的研究成果进行比较,结果表明本文所提模型优于对比模型,在两个数据集上的平均IOU分别达到了85%和84%,平均F1值分别达到了92%和93%,能够有效提高遥感图像目标分割的完整性和正确性。     

深度学习模型识别静止卫星图像海上强对流云团

强对流天气破坏力强,对海上航行和海洋开发都有着很大的影响,但由于其生命史短,且海上气象测站少,因此难以对其进行快速准确的监测。而静止气象卫星的覆盖范围广、时间分辨率高,因此成为了监测强对流天气的重要手段。本文提出了一种利用Himawari-8卫星影像,基于深度信念网络(DBN)进行强对流云团自动识别的方法。该方法分别提取每张图像的光谱特征TBB13、TBB08-TBB13和TBB13-TBB15,以及基于光谱特征TBB08-TBB13的纹理特征能量Energy和对比度Contrast,再参考CloudSat卫星的云分类产品自动构建样本集,利用此样本集训练DBN模型,以确定模型的参数和结构。使用训练完成的DBN模型进行强对流云团识别,并对识别结果进行后处理。通过典型案例分析和精度评定发现,新方法的临界成功指数CSI为71.28%,检测概率POD为84.83%,虚警率FAR为18.31%。结果表明,该方法可以有效识别处于初生到消散不同阶段的强对流云团,并在一定程度上去除检测结果中多余的卷云。与单波段阈值法、多波段阈值法和支持向量机这3种方法相比,文中提出的方法能够提高强对流云团的识别精度。     

A Bayesian characterization of urban land use configurations from VHR remote sensing images

The composition and arrangement of spatial entities, i.e., land cover objects, play a key role in distinguishing land use types from very high resolution (VHR) remote sensing images, in particular in urban environments. This paper presents a new method to characterize the spatial arrangement for urban land use extraction using VHR images. We derive an adjacency unit matrix to represent the spatial arrangement of land cover objects obtained from a VHR image, and use a graph convolutional network to quantify the spatial arrangement by extracting hidden features from adjacency unit matrices. The distribution of the spatial arrangement variables, i.e., hidden features, and the spatial composition variables, i.e., widely used land use indicators, are then estimated. We use a Bayesian method to integrate the variables of spatial arrangement and composition for urban land use extraction. Experiments were conducted using three VHR images acquired in two urban areas: a Pleiades image in Wuhan in 2013, a Superview image in Wuhan in 2019, and a GeoEye image in Oklahoma City in 2012. Our results show that the proposed method provides an effective means to characterize the spatial arrangement of land cover objects, and produces urban land use extractions with overall accuracies (i.e., 86% and 93%) higher than existing methods (i.e., 83% and 88%) that use spatial arrangement information based on building types on the Pleiades and GeoEye datasets. Moreover, it is unnecessary to further categorize the dominant land cover type into finer types for the characterization of spatial arrangement. We conclude that the proposed method has a high potential for the characterization of urban structure using different VHR images, and for the extraction of urban land use in different urban areas.     

利用车载LiDAR点云数据提取城市道路规则多边形

随着自动驾驶高精地图的发展,准确高效地提取道路规则多边形成为必要。本文以苏州市某城市快速路为研究对象,通过使用徕卡车载激光移动测量系统获得城市道路点云数据,观察了解车载点云数据各类属性信息,根据观察结果组合利用点云数据高差、点云灰度差值、点云强度差值,在此基础上再利用网格密度法实现道路规则多边形的提取,然后通过对目标的矢量化得到三维矢量规则多边形。最后对提取的矢量化结果进行精度评定。试验结果表明：该方法在提取道路规则多边形方面能得到较好的结果且能满足高精地图需求。     

城市交通热点区域识别及空间交互分析

出租车载客数据可以用于研究居民的出行特征,提取城市的交通热点区域,但对城市交通热点区域的交互关系研究相对较少。本文以纽约市的出租车行程记录数据为数据源,利用交通小区划分结合出租车载客数据提取城市交通热点区域,基于复杂网络的方法对不同日期类型和天气情况的城市交通热点区域空间交互网络进行研究并进行社区发现。结果表明,热点区域受城市核心区的影响而聚集在核心区域周围,城市内社区的形成可以克服地形和行政区域等因素的影响。研究结论有望为城市规划、城市交通管理、出租车调度、以及人们的出行等提供信息参考。     

丝绸之路经济带区域典型沙漠及绿洲监测分析

本文利用多期基础性地理国情监测正射影像成果,结合气象、统计等专题资料,借助遥感监测优势、测绘地理信息技术,对2012年、2016年、2017年沙漠范围及内部的绿洲变化情况进行了监测分析。结果表明:2012-2017年塔克拉玛干沙漠面积总体增加,增加幅度0.25%;占沙漠7.6%左右的绿洲,总体增加,增加幅度为4.79%,主要集中于沙漠南缘,沿河流分布、大小不一、彼此隔离;沙漠及内部绿洲变化形式复杂,不同区域变化趋势各不相同。整体来说,各县市区域内均以绿洲扩张、沙漠退缩为主,塔克拉玛干沙漠覆盖的各县市相较以往,生态环境有不同程度的改善。     

河口型水源地供水安全风险分类评估方法比选研究

为科学评估河口型水源地供水安全风险,基于水源地特点,在建立相应供水安全风险评估指标体系的基础上,采用层次分析法确定指标权重,结合选取灰色关联度法和BP神经网络法对某河口型水源地分别开展供水安全风险分类评估。以某次咸潮入侵事件数据为例,经采用两种方法评估后对比表明:灰色关联度法能较好地解决评价指标难以准确量化和统计的问题,具有定性与定量相结合评价精度较高的优点,更适合于河口型水源地供水安全风险分类评估。该水源地风险分类评价等级由高到低依次排序:水量风险(较高)→应急风险(较低)→工程风险(较低)→水质风险(低),其结果与实际相符。     

基于深度学习的高分四号卫星图像超分辨率重建

针对高分四号卫星图像中波红外谱段空间分辨率远低于相应的可见光近红外谱段,提出一种基于深度学习的高分四号卫星图像中波红外谱段超分辨率重建方法。首先,设计卷积回归网络进行初步重建,利用8倍下采样的可见光近红外谱段和原始中波红外谱段训练回归网络。然后,设计卷积重建网络进行进一步重建,利用低分辨率初步重建结果和原始中波红外谱段训练重建网络。最后,将原始可见光近红外谱段依次输入到训练好的回归网络和重建网络,得到最终的中波红外谱段超分辨率重建结果。以湖北和江西部分地区真实数据进行试验,结果表明该方法能有效提高中波红外谱段空间分辨率,与其他方法对比均方根误差至少下降了7.54,且目视效果更清晰自然,有利于扩展高分四号卫星的应用范围。