结合变差纹理特征的极化SAR建筑物震害信息提取

快速评估建筑物地震灾害信息对地震应急救援工作有着指导意义,而极化SAR具有全天候、全天时的特点,因此利用极化SAR图像提取震害信息已逐渐成为研究热点。虽然极化SAR具有丰富的极化信息,但其纹理信息不可忽略,尤其是完好的人工建筑物在图像上呈现规则的纹理特征,而倒塌建筑区域纹理分布杂乱,因此结合纹理信息也可以很好地提取建筑物信息。以2010年玉树地区的全极化SAR数据为研究对象,首先,利用Yamaguchi分解的体散射分量PV提取了SAR图像中的建筑物区域以及道路、水系等非建筑物信息,在此基础上,对相干散射矩阵T11分量中倒塌建筑物、完好建筑区域进行变差计算,根据变差曲线确定变程a后,再对建筑物区域采取窗口m*m(m=3*a)进行变差计算得到变差纹理信息,最后利用FCM算法对变差纹理信息分别提取完好建筑物和倒塌建筑物区域,为了对比分析,文章利用Yamaguchi分解的二次散射分量PD提取完好建筑物区域,与震后光学遥感图像对应样本点进行人工验证,得到完好建筑物的提取精度为80.18%,倒塌建筑物的提取精度为84.54%,道路水系的提取精度为77.58%。     

基于OSM的浮动车轨迹数据校正方法

浮动车轨迹数据具有覆盖范围广、更新周期短、获取成本低等特点，对于地图的生产和更新具有重要意义，但是由于受到卫星信号被遮挡及多路径效应的影响，其精度普遍较低。本文采用一种基于OSM作为参考数据的方式对浮动车轨迹数据进行校正。首先通过一种分层时空地图匹配的方式将轨迹数据与OSM进行匹配；然后采用引力模型对数据进行校正；最后在武汉市出租车轨迹数据上进行了试验。结果表明，本文提出的数据校正方法可以有效地提高浮动车轨迹数据的精度。     

一种移动视频与地理场景的融合方法

快速准确地了解灾害现场状况是救灾过程中的重中之重。通常发生灾害都会使用无人机进行现场勘察，但是无人机视频难以与实际的地理场景关联起来，为此本文提出了一种移动视频与地理场景的融合方法。该方法首先采用具有仿射不变性的ASIFT算法检测特征点，将匹配后的特征点采用RANSAC算法进行迭代剔除噪点，计算视频与地理场景最优的透视变换矩阵模型参数；然后将计算得到的透视变换参数应用到视频数据，恢复视频角点坐标；最后通过内插得出所有视频帧的角点坐标，实现视频与DOM的精确融合。试验结果表明，对视频数据匹配的间隔帧越短，其整体融合精度越高，通过本文方法进行视频与地理场景融合的误差标准差低于10 m。     

高分六号宽幅相机在轨几何定标及精度验证

高分六号宽幅相机能够实现单相机成像幅宽优于800 km,对大尺度地表观测和环境监测具有独特优势。在轨几何定标是光学遥感卫星几何处理的关键环节,直接影响影像的几何质量。本文充分考虑高分六号宽幅相机超大视场的畸变特性以及多谱段的成像特点,提出宽幅相机在轨几何定标方法,采用基于探元指向角的几何定标模型补偿宽幅相机系统误差,通过绝对定标和相对定标方法联合估计各波段的内外定标参数。利用Landsat 8影像、资源三号DSM为参考数据,对宽幅相机进行绝对定标处理,再利用ASTER GDEM为参考数据进行相对定标处理,其几何定标结果表明,高分六号宽幅影像绝对定位精度在3像素左右,内部几何精度能稳定在1像素,且波段间配准精度在0.3像素以内,表明在轨几何定标后高分六号宽幅影像几何质量得到了明显的提升。     

城市内涝灾害受灾人口评估方法与实证研究：以哈尔滨市道里区为例

城市内涝灾害频发,对居民生命安全造成严重威胁,为提高城市内涝灾害受灾人口评估精度,提出一种更为精确的受灾人口评估方法。以哈尔滨市道里区为研究区,以城市内涝灾害受灾人口为研究对象,运用一、二维非恒定流为主控方程,构建城市内涝数值模拟模型,并结合受灾人口分布特点,综合构建基于土地利用的人口随时间变化的计算模型。实现在模拟内涝灾害影响范围基础上,利用受灾人口计算模型提取白天与夜晚受灾人口分布情况。结果表明：研究区在百年一遇降雨情景下,有25条街道会发生不同程度积水,积水深度范围值为0.10~1.42 m;此级别内涝灾害在白天11条街道受灾人口数量最大值达到3 500人,夜晚10条街道受灾人口数量最大值为720人。     

基于地理核心素养的新高考地理试题分析与复习策略--以浙江省2019年4月选考29题为例

新高考地理试题不仅承载着选拔和教育功能,也是体现立德树人、检验学生核心素养达成与否的重要途径。本文以浙江省2019年4月选考综合题29题为例,从区域认知、综合思维、人地协调观、地理实践力四大地理学科核心素养角度进行分析,解读试题对核心素养的考查,并提出相应的复习策略,以期为地理教师指导学生高考复习提供导向。     

基于PCA-TOPSIS的重庆市县域经济密度综合测度及时空演变

经济密度是衡量区域经济发展水平和聚集程度的重要指标之一。以重庆市38个县（区）所辖范围为研究区，建立了人均和地均相结合的综合测度指标体系，采用主成分分析确定指标权重，将PCA TOPSIS评价法运用到经济密度综合测度中，依据测度对象与最优值的相对接近程度，综合测度1997年、2001年、2006年和2013年重庆市县域经济密度，获得各个时间断面经济密度综合指数及其排名。通过利用ArcGIS制图功能绘制重庆市经济密度等级分布图，揭示其空间分异演变规律，利用Moran’s I，LISA和热点分析分别探索了县域经济密度的全局和局部空间自相关及其时空演变规律，并讨论影响经济密度高低的主要因素。通过对县域经济密度的综合测度及时空演变的探究，为实现城市科学定位和制定中长期发展战略提供科学依据。     

Fe3O4/Mg(OH)2复合材料的制备及形貌控制

通过种子沉积法制备出Fe_3O_4/Mg(OH)_2复合材料并进行XRD、SEM测试分析。探究了氨水的浓度与加入速度、搅拌速度及等因素对磁性复合材料形貌的影响。氨水浓度与注入速度的降低,对氢氧化镁基体形貌的影响是相同的,但要使合成磁性复合材料形貌和四氧化三铁粒子在氢氧化镁中分散性均匀,应控制氨水浓度(ω=2.5%)或加入速度在一个合理的较低值。     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

Sampling Methane-Bearing Coal Seams by Freezing Method: Coalbed Methane Desorption and Inhibition Characteristics Under Freezing Temperature

Methane content in coal seam is an essential parameter for the assessment of coalbed gas reserves and is a threat to underground coal mining activities. Compared with the adsorption-isotherm-based indirect method, the direct method by sampling methane-bearing coal seams is apparently more accurate for predicting coalbed methane content. However, the traditional sampling method by using an opened sample tube or collecting drill cuttings with air drilling operation would lead to serious loss of coalbed methane in the sampling process. The pressurized sampling method by employing mechanical-valve-based pressure corer is expected to reduce the loss of coalbed methane, whereas it usually results in failure due to the wear of the mechanical valve. Sampling of methane-bearing coal seams by freezing was proposed in this study, and the coalbed gas desorption characteristics under freezing temperature were studied to verify the feasibility of this method. Results show that low temperature does not only improve the adsorption velocity of the coalbed gas, but also extend the adsorption process and increase the total adsorbed gas. The total adsorbed methane gas increased linearly with decreasing temperature, which was considered to be attributed to the decreased Gibbs free energy and molecular average free path of the coalbed gas molecular caused by low temperature. In contrast, the desorption velocity and total desorbed gas are significantly deceased under lower temperatures. The process of desorption can be divided into three phases. Desorption velocity decreases linearly at the first phase, and then, it shows a slow decreases at the second phase. Finally, the velocity of desorption levels off to a constant value at the third phase. The desorbed coalbed gas shows a parabolic relation to temperature at each phase, and it increases with increasing temperature at the first phase, and then, it poses a declining trend with increasing temperature at the rest phases. The experimental results show that decreasing the system temperature can restrain desorption of coalbed methane effectively, and it is proven to be a feasible way of sampling methane-bearing coal seams.