高分五号热红外传感器多通道SST反演

2018-05高分五号（GF-5）卫星发射升空,其上搭载的全谱段成像仪在热红外8—13 μm谱段范围内具有4个温度反演通道（B09,B10,B11,B12）,空间分辨率设计优于40 m,在国内民用传感器领域实现了由单通道向多通道、中空间分辨率向高空间分辨率的跨越式突破,使得GF-5卫星热红外数据在地表热环境遥感领域具有极其重要应用价值。本研究基于GF-5的4个热红外通道的通道响应函数,利用全球742条TIGR（Thermodynamic Initial Guess Retrieval）探空廓线数据,进行不同观测角度、水汽含量和海表发射率条件下的MODTRAN4.0（Moderate resolution atmospheric Transmittance and Radiance code4.0)辐射传输过程模拟,基于模拟结果分别对两通道、三通道和四通道劈窗算法海表温度SST（ Sea Surface Temperature）反演系数进行修订,并分析观测角度、水汽含量和海表发射率对不同通道组合的精度影响,并通过GF-5卫星实际反演的SST结果进行验证。GF-5全谱段成像仪SST反演两通道劈窗算法组合共有6种,即B09-B10、B09-B11、B09-B12、B10-B11、B10-B12、B11-B12;三通道劈窗算法组合共有4种,即B09-B10-B11、B09-B10-B12、B09-B11-B12、B10-B11-B12;四通道劈窗算法组合1种,即B09-B10-B11-B12。通过对不同通道组合形式研究发现,水汽含量对SST反演精度有较大的影响,且温度反演的精度随着水汽含量的增加而降低;其次是观测角度,SST反演精度随着观测天顶角的增大而降低;最后是发射率的影响,两通道、三通道和四通道劈窗算法SST反演精度随着发射率的变化总体在0.1 K以内变化。最后以大亚湾核电站周围海域为验证区,用GF-5热红外遥感影像进行SST的反演并做误差分析,结果表明B09-B10通道SST反演实际误差为0.57 K,反演精度较高,实际误差与理论模拟误差相差0.24 K,差异的来源主要包括辐射定标和传感器噪声等要素影响,其他通道形式反演精度有待于传感器响应稳定后进一步验证。     

Tile‐based polygon overlay of vector data in a virtual globe

The demand for analysis of large‐scale data has increased with increased access to spatial vector data. Polygon overlay of vector data in a virtual globe requires proprietary data structures and proprietary analysis algorithms. A vector data structure is designed for rapid polygon overlay in a virtual globe by recording the metadata of the triangles (TriMeta) that constitute polygons. A polygon overlay algorithm is proposed based on this data structure. The overlay of two complex GIS polygons is transformed into the intersection computation of their 3D triangular presentations. The intersection computation of two sets of triangles is reduced considerably by utilizing TriMeta to filter out the disjoint triangle pairs and rapidly identify the contained triangle. The new method improves the overlay efficiency in a virtual globe because the amount of computation required to calculate the intersections of two large polygons and drape the intersections onto a terrain surface is reduced.     

A probabilistic framework for improving reverse geocoding output

Reverse geocoding, which transforms machine‐readable GPS coordinates into human‐readable location information, is widely used in a variety of location‐based services and analysis. The output quality of reverse geocoding is critical because it can greatly impact these services provided to end‐users. We argue that the output of reverse geocoding should be spatially close to and topologically correct with respect to the input coordinates, contain multiple suggestions ranked by a uniform standard, and incorporate GPS uncertainties. However, existing reverse geocoding systems often fail to fulfill these aims. To further improve the reverse geocoding process, we propose a probabilistic framework that includes: (1) a new workflow that can adapt all existing address models and unitizes distance and topology relations among retrieved reference data for candidate selections; (2) an advanced scoring mechanism that quantifies characteristics of the entire workflow and orders candidates according to their likelihood of being the best candidate; and (3) a novel algorithm that derives statistical surfaces for input GPS uncertainties and propagates such uncertainties into final output lists. The efficiency of the proposed approaches is demonstrated through comparisons to the four commercial reverse geocoding systems and through human judgments. We envision that more advanced reverse geocoding output ranking algorithms specific to different application scenarios can be built upon this work.     

Small‐area patch‐merging method accounting for both local constraints and the overall area balance

Small‐area patch merging is a common operation in land use data generalization. However, existing research on small‐area patch merging has mainly focused on local compatibility measures, which often lead to area imbalances among land use types from a global perspective. To address the shortcomings of previous studies by resolving local and global concerns simultaneously, this article proposes a merging method that considers both local constraints and the overall area balance. First, a local optimization model that considers three constraints—namely, the areas of neighboring patches, the lengths of shared arcs, and semantic similarity—is established. The areas of small patches are first pre‐allocated. Subsequently, in accordance with an area change threshold for individual land use types, land use types with area changes that exceed this threshold are identified. The patches corresponding to these land use types are subjected to iterative adjustments while considering the overall area balance. Based on their area splitting abilities, the split lines for small‐area patches are determined, and small‐area patches are merged. Finally, actual data from Guangdong Province are used for validation. The experimental results demonstrate that the proposed method is capable of preserving the local compatibility of patches while balancing the overall area associated with each land use type.     

A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data

ABSTRACT

Earth observations and model simulations are generating big multidimensional array-based raster data. However, it is difficult to efficiently query these big raster data due to the inconsistency among the geospatial raster data model, distributed physical data storage model, and the data pipeline in distributed computing frameworks. To efficiently process big geospatial data, this paper proposes a three-layer hierarchical indexing strategy to optimize Apache Spark with Hadoop Distributed File System (HDFS) from the following aspects: (1) improve I/O efficiency by adopting the chunking data structure; (2) keep the workload balance and high data locality by building the global index (k-d tree); (3) enable Spark and HDFS to natively support geospatial raster data formats (e.g., HDF4, NetCDF4, GeoTiff) by building the local index (hash table); (4) index the in-memory data to further improve geospatial data queries; (5) develop a data repartition strategy to tune the query parallelism while keeping high data locality. The above strategies are implemented by developing the customized RDDs, and evaluated by comparing the performance with that of Spark SQL and SciSpark. The proposed indexing strategy can be applied to other distributed frameworks or cloud-based computing systems to natively support big geospatial data query with high efficiency.     

车辆轨迹数据的道路学习提取法

车辆轨迹数据的道路信息提取是地理信息领域的热点也是难点之一,深度学习的快速发展为该问题的解决提供了一种思路与方法。本文针对车辆轨迹数据的车行道级道路提取问题,引入深度学习领域的生成式对抗网络,利用残差网络构建深层网络和多尺度感受野感知轨迹数据不同细节特征,构建了基于条件生成式对抗网络的轨迹方向约束下车行道级道路提取模型。首先提出了朝向-颜色映射栅格化转换方法,实现轨迹朝向信息向HSV颜色空间的转换;然后利用样本数据学习模型参数;最后将训练模型应用到郑州、成都、南京3个试验区域提取车行道级道路数据。试验结果表明,本文方法能够有效地提取完整的车行道级道路数据。     

基于中性集对北川羌族自治县新城人工建设用地的识别

人工建设用地(包括建筑物、道路、广场等社会服务设施)的识别一直是用来监测地区发展速度的一个有效途径。针对目前在人工建设用地识别领域中对在建建筑物的忽视问题,利用中性集、均值漂移以及绿度因子等概念将在建建设用地信息进行增强,进而将其成功识别出来。实验证明,该方法对高分辨率遥感影像的人工建设用地识别是可行的。通过分析2009—2013年期间北川新城的建设工地面积及分布的变化情况可以看出,北川新城在2010—2013年期间完工面积占2009—2010年新建工程总面积的98.17%,在北川新城拓展区又新建0.6 km2的工程,施工迅速,为受灾居民提供了良好的居住和生活保障。     

基于原始观测值的单频精密单点定位算法

研究了一种基于GPS原始观测值的单频PPP算法。该算法通过增加电离层延迟先验信息、空间和时间约束的虚拟观测方程,将电离层延迟当作未知参数与其他定位参数一并进行估计来高效修正电离层延迟误差。通过使用全球178个IGS站1d的实测数据对本算法的收敛速度、定位精度和电离层VTEC的精度进行检验与分析。结果表明,该算法的收敛速度和稳定性均得到了改善,其静态单频单天PPP解的精度可达2~3cm、模拟动态单频单天PPP解的精度可达2~3dm,并且单频PPP与双频PPP提取的电离层总电子含量平均偏差小于5个TECU,可作为一种附属定位产品使用。     

自然资源全要素自适应野外调查框架设计

针对自然资源全要素调查工作中分类标准不同、调查口径不同、调查方法不同,造成的各部门调查软件平台重复开发建设和调查数据结果地理信息特征表达不清晰等问题,本文结合自然资源全要素野外调查工作的实际需要,以山水林田湖草生命共同体为统一对象,构建了自然资源语义化特征模型,提出了表征不同自然资源类型的自适应野外调查方法,实现了灵活性高、性能优异的自然资源调查技术平台,形成了统一的自然资源全要素野外调查框架,可以有效提升多门类自然资源基础调查、专业调查、动态调查的能力与水平,为自然资源治理能力现代化提供了可靠技术保障。     

多源地理空间矢量数据关联分析

针对多源地理空间矢量数据多来源、难以集成综合利用这一现状,本文提出了多源地理空间矢量数据关联方法,并以此为基础构建了多源地理空间矢量数据关联的可视与计算查询系统。首先,对多源地理空间矢量数据关联的概念及分类进行了定义。然后,以此为基础,提出了关联关系构建技术：自适应四叉树编码技术、扫描线技术、几何匹配及语义匹配技术。最后,为实现关联关系的直观展示,设计了原型系统。关联技术的提出可建立起多源地理空间矢量数据之间的关联关系,原型系统的构建也为用户综合利用多源地理空间矢量数据提供了平台,提高了数据的利用率及数据查询的效能。