Improved Priority‐Flood method for depression filling by redundant calculation optimization in local micro‐relief areas

Depression filling is a critical step in distributed hydrological modeling using digital elevation models (DEMs). The traditional Priority‐Flood (PF) approach is widely used due to its relatively high efficiency when dealing with a small‐sized DEM. However, it seems inadequate and inefficient when dealing with large high‐resolution DEMs. In this work, we examined the relationship between the PF algorithm calculation process and the topographical characteristics of depressions, and found significant redundant calculations in the local micro‐relief areas in the conventional PF algorithm. As such calculations require more time when dealing with large DEMs, we thus propose a new variant of the PF algorithm, wherein redundant points and calculations are recognized and eliminated based on the local micro‐relief water‐flow characteristics of the depression‐filling process. In addition, depressions and flatlands were optimally processed by a quick queue to improve the efficiency of the process. The proposed method was applied and validated in eight case areas using the Shuttle Radar Topography Mission digital elevation model (SRTM‐DEM) with 1 arc‐second resolution. These selected areas have different data sizes. A comparative analysis among the proposed method, the Wang and Liu‐based PF, the improved Barnes‐based PF, the improved Zhou‐based PF, and the Planchon and Darboux (P&D) algorithms was conducted to evaluate the accuracy and efficiency of the proposed algorithm. The results showed that the proposed algorithm is 43.2% (maximum) faster than Wang and Liu's variant of the PF method, with an average of 31.8%. In addition, the proposed algorithm achieved similar performance to the improved Zhou‐based PF algorithm, though our algorithm has the advantage of being simpler. The optimal strategies using the proposed algorithm can be employed in various landforms with high efficiency. The proposed method can also achieve good depression filling, even with large amounts of DEM data.     

从规则格网DEM自动提取汇水区域及其子区域的方法

从DEM自动提取的汇水区域及其子区域信息对进一步的水文分析有着重要的辅助作用.在经过洼地处理的DEM及得到水流方向矩阵的基础上,本文提出并实现了一种汇水区域及其子区域的提取算法.该算法首先提取整个DEM区域内每条河流的汇水区域,然后按照不同的要求划分子区域.经过试验,与现有的方法相比,该算法在提取效率和结果准确性方面都有明显提高.     

基于数字高程模型的混合流向算法

从数字高程模型提取的汇水网络和汇水区等信息是分布式水文模型及应用分析的基础参数,基于地表汇水模拟的算法是提取该类信息的主要方法,其中,水流方向的确定对提取结果有着直接的影响。单流向算法因其易于实现、易于确定上游汇水区等特性,得到了广泛应用,然而单流向算法在坡度平缓区域会产生不自然的平行径流,能模拟地表水流分散径流特点的多流向算法可以在一定程度上避免此问题,但多流向算法使得不同区域的汇水单元可能存在交叉。本文结合两类流向算法各自的优点和适用性,设计实现了一种混合流向算法,以期在不同的地形条件下模拟得到更加合理的水流分配。首先,使用基于模板的形态检测方法,在给定阈值的基础上,对数字地形进行了分类,DEM被划分为山谷、山脊、鞍部、缓坡和陡坡5类。对陡坡、山谷和山脊区域运用单流向算法;对缓坡和鞍部区域采用多流向算法确定径流方向并进行水量分配。本文选取了黄土地貌和中低山丘陵的两个流域作为研究区,利用并采用了30 m和90 m两个分辨率的DEM。本文研究将混合流向算法与现有其他算法的结果进行比较。相比于多流向算法,该算法结果中的分散效应受到明显的抑制,相比于单流向算法,非自然的平行径流也大幅减少。同时,混合流向算法在较大分辨率DEM上（30 m）改进效果更加明显。     

基于规则格网DEM的地形特征提取算法

提出并实现了一种基于规则格网DEM的地形特征线提取算法,该算法在现有地表水流模拟方法的基础上将矢量操作与栅格操作结合起来对规则格网DEM中的洼地进行填平处理,采用邻域格网分组扫描方法确定平地水流方向,与以往算法相比在提取效率和结果准确性等方面都有了明显的改进,从而也使其更加适合于对大规模的DEM数据进行处理,采用各种尺度的DEM进行试验的结果验证了以上结论.     

Urban DEM generation,analysis and enhancements using TanDEM-X

This paper analyzes the potential of the TanDEM-X mission for the generation of urban Digital Elevation Models (DEMs). The high resolution of the sensors and the absence of temporal decorrelation are exploited. The interferometric chain and the problems encountered for correct mapping of urban areas are analyzed first. The operational Integrated TanDEM-X Processor (ITP) algorithms are taken as reference. The ITP main product is called the raw DEM. Whereas the ITP coregistration stage is demonstrated to be robust enough, large improvements in the raw DEM such as fewer percentages of phase unwrapping errors, can be obtained by using adaptive fringe filters instead of the conventional ones in the interferogram generation stage. The shape of the raw DEM in the layover area is also shown and determined to be regular for buildings with vertical walls. Generally, in the presence of layover, the raw DEM exhibits a height ramp, resulting in a height underestimation for the affected structure. Examples provided confirm the theoretical background. The focus is centered on high resolution DEMs produced using spotlight acquisitions. In particular, a raw DEM over Berlin (Germany) with a 2.5 m raster is generated and validated. For this purpose, ITP is modified in its interferogram generation stage by adopting the Intensity Driven Adaptive Neighbourhood (IDAN) algorithm. The height Root Mean Square Error (RMSE) between the raw DEM and a reference is about 8 m for the two classes defining the urban DEM: structures and non-structures. The result can be further improved for the structure class using a DEM generated with Persistent Scatterer Interferometry. A DEM fusion is thus proposed and a drop of about 20% in the RMSE is reported.     

An improved tree crown delineation method based on live crown ratios from airborne LiDAR data

Individual tree crowns are one of the basic forest inventory data, which can be used in various forest-related studies such as biomass and carbon stock estimation. High-resolution remote-sensing data including airborne LiDAR-derived surfaces have been widely used for delineating tree crowns. This study proposes an improved tree crown delineation algorithm that can be effectively applied to a range of forests with a limited number of parameters considering its operational use with airborne LiDAR data. The proposed algorithm integrates morphological operators, Otsu’s method, marker-controlled watershed segmentation, and the concept of crown ratios. The proposed algorithm was compared with the region growing method, a widely used tree crown delineation algorithm. The two algorithms were evaluated over 10 plots in rugged terrain located in Kangwon Province in South Korea. Results show that the proposed approach produced much better performance (~87% matched on average) for 10 plots with a range of tree densities than the region growing method (~60% matched on average). The proposed algorithm worked better for sparse plots than dense ones. It also worked well for deciduous plots (plots 1 and 4). On the other hand, the region growing method produced relatively low accuracy with many merged crowns, which requires additional postprocessing such as a resplit step.     

面向智能手机的改进有限状态机步态探测算法

针对室内定位行人航位推算中步态探测算法步数识别准确率不高、同步控制不精确及位置估计偏差较大等问题,提出了一种面向智能手机平端活动的改进有限状态机步态探测算法。通过设定有限状态对应步行过程合加速度变化趋势,利用相邻合加速度差值和上/下坡次数阈值实现步数识别和步态周期估计。在211 m走廊内由2名实验人员分别平端智能手机开展实验,结果表明,改进算法的步数识别准确率为100%,每一步的平均识别时间提前了0.004 s,平均位置误差为0.384 m,相比于自相关分析和加速度差分有限状态机算法,识别准确率、同步控制精度和位置估计精度分别至少提高了0.7%、60%和21.15%。改进算法在步数识别、同步控制及位置估计方面优于现有算法。     

基于变量投影的结构总体最小二乘算法

测绘领域诸多实际应用中系数矩阵和观测向量具有结构特征,即系数矩阵和观测向量中包含固定量(甚至固定列)和随机量,并且不同位置的随机量线性相关。针对这个问题,从变量误差(errors-in-variables,EIV)函数模型出发,首先,将系数矩阵和观测向量构成的增广矩阵表示为仿射函数形式,并采用变量投影法对函数模型进行重构;然后,利用拉格朗日法推导出了一种结构总体最小二乘(structured total least squares,STLS)估计算法。算例分析结果表明,该算法与已有能够解决系数矩阵和观测向量存在结构特征的加权或结构总体最小二乘算法估计结果一致,说明了该算法的有效性,同时阐明了该算法与已有相关算法的关系。     

An enhanced approach for surface flow routing over drainage‐constrained triangulated irregular networks

The accuracy and efficiency of the simulations in distributed hydrological models must depend on the proper estimation of flow directions and paths. Numerous studies have been carried out to delineate the drainage patterns based on gridded digital elevation models (DEMs). The triangulated irregular network (TIN) has been increasingly applied in hydrological applications due to the advantages of high storage efficiency and multi‐scale adaptive performance. Much of the previous literature focuses mainly on filling the depressions on gridded DEMs rather than treating the special cases in TIN structures, which has hampered its applications to hydrological models. This study proposes a triangulation‐based solution for the removal of flat areas and pits to enhance the simulation of flow routing on triangulated facet networks. Based on the drainage‐constrained TIN generated from only a gridded DEM by the compound point extraction (CPE) method, the inconsistent situations including flat triangles, V‐shape flat edges and sink nodes are respectively identified and rectified. The optimization algorithm is an iterative process of TIN reconstruction, in which the flat areas are generalized into their center points and the pits are rectified by embedding break lines. To verify the proposed algorithm and investigate the potential for flow routing, flow paths of steepest descent are derived by the vector‐based tracking algorithm based on the optimized TIN. A case study of TIN optimization and flow path tracking was performed on a real‐world DEM. The outcomes indicate that the proposed approach can effectively solve the problem of inconsistencies without a significant loss in accuracy of the terrain model.     

Spheroidal equal angular DEMs: The specificity of morphometric treatment

Digital elevation models (DEMs) are commonly constructed using two main types of regular grids: plane square grids and spheroidal equal angular grids. Methods and algorithms intended for plane square‐gridded DEMs should not be directly applied to spheroidal equal angular DEMs. This is because these grids have fundamentally different geometry. However, some researchers continue to apply square‐grid algorithms to spheroidal equal angular DEMs. It seems appropriate to consider once again the specifity of morphometric treatment of spheroidal equal angular DEMs. This article, first, demonstrates possibilities of direct calculation of local, nonlocal, and combined morphometric variables from spheroidal equal angular DEMs exemplified by slope gradient, catchment area, and topographic index. Second, the article shows computational errors when algorithms for plane square‐gridded DEMs are unreasonably applied to spheroidal equal angular DEMs. The study is exemplified by two DEMs. A medium‐resolution DEM of a relatively small, high‐mountainous area (Mount Elbrus) was extracted from the SRTM1 DEM. A low‐resolution DEM of a vast region with the diverse topography (the central and western regions of Kenya) was extracted from the SRTM30_PLUS DEM. The results show that application of square‐grid methods to spheroidal equal angular DEMs leads to substantial computational errors in models of morphometric variables.     

DEM融合算法及其在困难地区测图中的应用

对于我国西部高山区,如横断山脉,高程起伏明显,常年被云雾覆盖,日照稀少,采用传统方法进行地形图测绘存在较大困难,依赖单一方法获取的DEM往往难以满足测图的精度要求。为充分利用不同传感器和不同方法生成的DEM的优点,本文根据各方法的特点,结合小比例尺地形图中低精度的DEM,基于绝对精度等先验知识确定优先级别、相关/干系数确定融合权重,提出了一种包括雷达干涉测量、光学立体摄影测量、不同侧视方向像对雷达立体测图生成的四种多源DEM的像素级融合算法。在横断山脉地区使用所提融合算法进行了实验,获得了一个总体精度得到提高的无缝DEM,实验结果表明新算法为地形复杂的测图困难地区DEM获取提供了一种可能的解决方案。     

FAN模型的双种群差分进化光谱解混算法

双线性混合模型是近年来非线性光谱解混的研究重点之一,其克服了线性混合模型无法描述地物多重散射作用的缺陷,能够更精确地还原真实的地物光谱混合过程。然而,限于模型的复杂性,目前在缺乏准确的端元先验知识的条件下进行双线性光谱解混仍是一项具有挑战性的任务。差分进化算法(DE)是一种具有良好全局搜索能力的群智能优化算法,其优化求解过程无需进行复杂的数学推导,为双线性光谱解混问题提供了一种有效的解决途径。为此,本文以FAN双线性混合模型为例,提出了一种双种群机制的差分进化算法(记为DEFAN),实现非监督双线性光谱解混。DE-FAN算法通过建立端元与丰度两个种群的交替进化机制寻找最优解,同时在迭代中引入自适应重构策略增强种群多样性,降低算法陷入局部最优解的风险,最终实现端元与丰度的同时估计。通过模拟图像及真实图像的解混实验进行算法检验,证明DE-FAN算法较之传统非线性解混算法具有更高的解混精度及解混效率。     

Open source map matching with Markov decision processes: A new method and a detailed benchmark with existing approaches

Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.     

引入曲面变分实现点云法矢一致性调整

针对现有法矢一致性调整算法效率不高、准确度低的问题,引入曲面变分改善该类算法的性能。首先通过主成分分析法估算点云的法矢和曲面变分,然后用曲面变分区分平缓点与非平缓点。调整法矢时,采用缩小待调整法矢的搜索范围到k-邻域内和增加每次搜索时法矢传播个数的策略来提高效率;采用约束法矢传播方向的方法保证准确性。试验结果表明,该算法在平缓区域、尖锐特征区域和高曲率区域均能得到正确的法矢调整结果,且算法效率较已有方法更高。     

一种基于小波变换的GPS基线求解算法

在分析ＧＰＳ相位观测粗差,周跳及随机噪声的小波变换特性基础上,提出了基于小波变换的ＧＰＳ相位粗差探测及小波滤波算法,并提出了基于波滤波的ＧＰＳ基线求解算法,该方法能缩小模糊度搜索空间,提高整数模糊度解的有效性。     

加速迭代硬阈值ISAR稀疏成像

加速迭代硬阈值(AIHT)作为一种压缩感知(CS)算法,具有计算复杂度低、内存占用小的特点,在较小数目的观测下,经过有限次迭代便能获得较好的重建效果。因此,本文将该算法应用到逆合成孔径雷达(ISAR)稀疏成像,对该方法的具体特征和适用性进行了详细分析。仿真结果表明,在相同信噪比和回波脉冲数条件下,基于AIHT的ISAR成像方法,相比于稀疏贝叶斯算法(SBL),在重建时间上缩短了至少2个量级;相比于迭代加权最小二乘法(IRLS)和平滑l0范数算法,在重建质量上提高了约3倍。经实测数据处理结果验证,在1/4原始脉冲数条件下,该方法仍具有良好的聚焦性能和压低旁瓣效果。     

基于FPGA的P-H法星上解算卫星相对姿态

针对目前星上遥感图像实时处理只能实现低级别算法的情况,提出了基于现场可编程门阵列（field-programmable gate array,FPGA）的P-H法星上相对姿态实时解算模型。该模型不仅避免了传统基于欧拉角的复杂三角函数计算与初值估算,还降低了迭代次数。试验选用FPGA（V7 xc7vx1140t）作为实时解算的硬件平台。在FPGA实现中,采用64位的浮点数据结构和串行/并行相结合策略;并采用LU（Lower-Upper）分解-分块算法实现矩阵求逆。试验结果表明,该模型的迭代次数比基于欧拉角的少了13次。该模型在FPGA和计算机的实现结果相差仅为5.0×10-14,加速度比为10。另外,该模型可广泛适用于实时性要求高的图像处理领域。     

DEM Fusion using a modified k-means clustering algorithm

Digital elevation models (DEMs) are a necessary dataset for modelling the Earth’s surface; however, all DEMs contain error. Researchers can reduce this error using DEM fusion techniques since numerous DEMs can be available for a region. However, the use of a clustering algorithm in DEM fusion has not been previously reported. In this study a new DEM fusion algorithm based on a clustering approach that works on multiple DEMs to exploit consistency in the estimates as indicators of accuracy and precision is presented. The fusion approach includes slope and elevation thresholding, k-means clustering of the elevation estimates at each cell location, as well as filtering and smoothing of the fusion product. Corroboration of the input DEMs, and the products of each step of the fusion algorithm, with a higher accuracy reference DEM enabled a detailed analysis of the effectiveness of the DEM fusion algorithm. The main findings of the research were: the k-means clustering of the elevations reduced the precision which also impacted the overall accuracy of the estimates; the number of final cluster members and the standard deviation of elevations before clustering both had a strong relationship to the error in the k-means estimates.     

基于参考面的可视域算法

与传统基于视线的可视域算法相比，该算法不需进行DEM内插，无冗余计算，计算时间和视点位置与可视域面积无关，受DEM区域影响不大。