基于多时相影像的农业作物非参数与概率分类

本文提出了一种新的结合多光谱和变化检测技术的多时相卫星数据集分类方法。该方法以数理统计中的最近邻法为基础,其目标函数是使得正确分类的平均概率得到最优化,即把每个分类类别看成同等重要。该新算法被应用于一个农业作物分类的研究区域,并利用覆盖该区的不同季节的SPOT和LANDSAT TM多时像影像。结果表明,与单时像影像相比,使用五个不同季节的多时像影像可以充分地提高分类精度。为了说明该方法在大尺度范围内的效果,本文选取瑞典道拉河流域作为研究区。由于不同地物的分布高度重叠,不可能得到像元水平上满意的分类精度。这就需要引进一种新的概念：像元概率分类法。基于像元的概率向量可用于判别传统分类法的可靠性并测量单个像元的不确定性（熵）。概率分类法同时提供了不同地物的面积的无偏估计,无论所感兴趣的区域的大小。这已经在不同特性的耕地试验点进行了检验。     

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.     

DBSTC: an effective method for discovering cluster features with different spatiotemporal densities

Spatiotemporal clustering is one of the most advanced research topics in geospatial data mining. It has been challenging to discover cluster features with different spatiotemporal densities in geographic information data set. This paper presents an effective density-based spatiotemporal clustering algorithm (DBSTC). First, we propose a method to measure the degree of similarity of a core point to the geometric center of its spatiotemporal reachable neighborhood, which can effectively solve the isolated noise point misclassification problem that exists in the shared nearest neighbor methods. Second, we propose an ordered reachable time window distribution algorithm to calculate the reachable time window for each spatiotemporal point in the data set to solve the problem of different clusters with different temporal densities. The effectiveness and advantages of the DBSTC algorithm are demonstrated in several simulated data sets. In addition, practical applications to seismic data sets demonstrate the capability of the DBSTC algorithm to uncover clusters of foreshocks and aftershocks and help to improve the understanding of the underlying mechanisms of dynamic spatiotemporal processes in digital earth.     

卫星遥感融合中通量守恒重采样方法与其它常用方法的比较

对多卫星传感器数据进行融合,首先要将多个传感器数据通过重采样算法重新投影到标准网格上。本文运用一种基于多边形切割算法的通量守恒重采样算法对图像数据进行重采样,并将该算法与3种常用的重采样算法(最邻近插值法、双线性插值法、三次卷积插值法)在信息保真方面的性能进行了比较。将所比较的重采样方法应用于两幅具有代表性的图像,其中一幅为人造图像,用于定性比较各种采样方法在图像缩放中的采样精度;另一幅为某机场卫星遥感图像,用于评价各种重采样方法在旋转图像方面采样的性能,并以定量参数(相关系数及光谱真实性)比较各种采样方法。结果表明,通量守恒重采样法对原始图像的信息保真效果最好,更适用于卫星遥感图像数据融合中的重采样。     

基于SAR和AIS的角度最近邻数据关联方法

提升海上态势感知能力是构建智慧海洋的重要环节。针对目前海上目标研究单源传感器存在感知盲区,多源传感器数据关联易受杂波干扰、在密集区表现不佳等问题,本文基于合成孔径雷达(SAR)和船舶自动识别系统(AIS)数据,提出一种抗干扰性强的角度最近邻数据关联方法,充分利用SAR与AIS船舶目标的空间角度关系,提高船舶目标在密集区域点迹关联的准确性。首先,对AIS数据进行时空滤波,实现数据粗关联,构建关联分析的数据候选集;然后,从时空数据的空间关系角度出发,在灰狼优化和匈牙利算法的启发下,利用点迹对特征向量矩阵进行运算,实现对多源空间数据的优化关联;最后结合数据几何关系对结果进行置信度评估。本文选取5幅SAR影像与AIS数据进行实验,并基于SAR影像数据及船舶轨迹点分布密度设计仿真实验,结果表明,本文所提出的角度最近邻数据关联方法,在密集分布情况下,关联精度为传统NN、GNN算法的3.62和4.61倍,运行时间为1.69 s,相较于NN算法仅增长1.36 s,仅占GNN运行时间的0.49%,在运行时间增长不大的情况下具有更强的抗干扰能力,在密集区域仍能取得较好的关联效果。     

基于多类型无人机数据的红树林遥感分类对比

使用固定翼无人机、消费级旋翼无人机和专业级旋翼无人机获取广东珠海淇澳岛红树林保护区多类型无人机遥感影像,使用基于面向对象分类的K-最近邻与随机森林分类器对研究区影像进行红树林树种精细分类和对比分析,并探讨了不同类型无人机平台在红树林资源调查应用中的优缺点。结果表明：1）固定翼无人机、消费级旋翼无人机和专业级旋翼无人机数据使用K-最近邻法的分类精度分别为：73.8%、72.8%和79.7%;使用随机森林法的分类精度分别为：81.1%、84.8%和89.3%。3种平台类型的无人机数据均适用于红树林精细分类研究,对于无人机红树林遥感数据,随机森林的分类方法优于K-最近邻方法。2）以拍摄面积与用时之比估算采集效率,固定翼无人机、消费级旋翼无人机和专业级旋翼无人机分别为0.036、0.013和0.003 km2/min。固定翼无人机的采集效率具有明显优势。3）固定翼无人机适合大范围红树林数据采集,要求较高;消费级旋翼无人机适于获取小范围精细数据,成本低且易学易用;专业级旋翼无人机适合搭载质量稍大的如成像光谱仪、LiDAR等专业传感器获取多源数据。最后给出了无人机在红树林遥感研究中的注意事项和建议。     

秦岭-大别及邻区S波速度及其在秭归地震定位中的应用

在秦岭-大别造山带及邻区背景噪声相速度成像的基础上,用近邻算法反演得到深度6～38 km范围内的S波速度分布图像。依据S波速度结构建立湖北分区速度模型,并将该模型应用到2017-02-23秭归3.8级地震和06-16秭归4.3级地震定位中。结果显示,分区模型得到的总体平均误差比一维模型的更小,与三峡台网定位结果相比分区模型得到的定位结果偏差也更小。     

相邻多边形共享边界的一致化改正

针对相邻多边形叠置不能保证精确的拓扑邻近 ,往往产生大量的“碎片”和“缝隙” ,破坏了面状目标间的拓扑一致性这一问题。依据相邻多边形之间的空间关系 ,将共享边界不一致区分为相交型、相离型、交织型 ,将拓扑一致化处理的操作分为咬合式处理和平差式处理 ,并基于Delaunay三角网模型邻近分析 ,探测由三角形集表达的边界不一致局部区域 ,通过三角网骨架线提取来进行边界不一致改正。     

A Fixed-Path Markov Chain Algorithm for Conditional Simulation of Discrete Spatial Variables

The Markov chain random field (MCRF) theory provided the theoretical foundation for a nonlinear Markov chain geostatistics. In a MCRF, the single Markov chain is also called a “spatial Markov chain” (SMC). This paper introduces an efficient fixed-path SMC algorithm for conditional simulation of discrete spatial variables (i.e., multinomial classes) on point samples with incorporation of interclass dependencies. The algorithm considers four nearest known neighbors in orthogonal directions. Transiograms are estimated from samples and are model-fitted to provide parameter input to the simulation algorithm. Results from a simulation example show that this efficient method can effectively capture the spatial patterns of the target variable and fairly generate all classes. Because of the incorporation of interclass dependencies in the simulation algorithm, simulated realizations are relatively imitative of each other in patterns. Large-scale patterns are well produced in realizations. Spatial uncertainty is visualized as occurrence probability maps, and transition zones between classes are demonstrated by maximum occurrence probability maps. Transiogram analysis shows that the algorithm can reproduce the spatial structure of multinomial classes described by transiograms with some ergodic fluctuations. A special characteristic of the method is that when simulation is conditioned on a number of sample points, simulated transiograms have the tendency to follow the experimental ones, which implies that conditioning sample data play a crucial role in determining spatial patterns of multinomial classes. The efficient algorithm may provide a powerful tool for large-scale structure simulation and spatial uncertainty analysis of discrete spatial variables.     

基于机器学习技术的蒸发皿蒸发量估算模型

为了弥补国家级气象观测站小型蒸发皿停止观测后蒸发量观测资料的空缺,建立了陕北、关中和陕南3个区域数据集以及榆林、泾河和汉中3个单站数据集,通过建立和优化KNN、MLP模型及其参数,分别建立蒸发量区域估算模型、单站估算模型并对其进行检验.结果 表明:①进行区域蒸发量估算时,KNN模型表现出良好的泛化性能,均方误差、总相对...