基于超像元词包特征和主动学习的高分遥感影像变化检测     

杨进一  徐伟铭  王成军  翁谦 《地球信息科学学报》2019,21(10):1594-1607

为解决高分辨率遥感影像变化检测中存在底层特征缺乏语义信息、像元级的检测结果存在“椒盐”现象以及监督分类中样本标注自动化程度较低,本文提出一种基于超像元词包特征和主动学习的变化检测方法。首先采用熵率分割算法获取叠加影像的超像元对象;其次提取两期影像像元点对间的邻近相关影像特征（相关度、斜率和截距）和顾及邻域的纹理变化强度特征（均值、方差、同质性和相异性）,经线性组合作为像元点对的底层特征;然后基于像元点对底层特征利用BOW模型构建超像元词包特征,并采用一种改进标注策略的主动学习方法从无标记样本池中优选信息量较大的样本,且自动标注样本类别;最后训练分类器模型完成变化检测。通过选用2组不同地区的GF-2影像和Worldview-Ⅱ影像作为数据源进行实验,实验结果中2组数据集的F1分数分别为0.8714、0.8554,正确率分别为0.9148、0.9022,漏检率分别为0.1681、0.1868,误检率分别为0.0852、0.0978。结果表明,该法能有效识别变化区域、提高变化检测精度。此外,传统主动学习方法与改进标注策略的主动学习方法的学习曲线对比显示,改进的标注策略可在较低精度损失下,有效提高样本标注自动化程度。  相似文献   

长江河口潮波传播机制及阈值效应分析   总被引：1，自引：0，他引：1  

张先毅  黄竞争  杨昊  欧素英  刘锋  蔡华阳  杨清书 《海洋与湖沼》2019,50(4):788-798

河口潮波传播过程受沿程地形(如河宽辐聚、水深变化)及上游径流等诸多因素影响,时空变化复杂。径潮动力非线性相互作用研究有利于揭示河口潮波传播的动力学机制,对河口区水资源高效开发利用具有重要指导意义。本文基于2007—2009年长江河口沿程天生港、江阴、镇江、南京、马鞍山、芜湖的逐日高、低潮位数据及大通站日均流量数据,统计分析不同河段潮波衰减率与余水位坡度随流量的变化特征,结果表明潮波衰减率绝对值与余水位坡度随流量增大并不是单调递增,而是存在一个阈值流量和区域,对应潮波衰减效应的极大值。为揭示这一阈值现象,采用一维水动力解析模型对研究河段的潮波传播过程进行模拟。结果表明,潮波传播的阈值现象主要是由于洪季上游回水作用随流量加强,余水位及水深增大,导致河口辐聚程度减小,而余水位坡度为适应河口形状变化亦有所减小,从而形成相对应的阈值流量和区域。  相似文献   

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process     

《地学前缘(英文版)》2020,11(6):2207-2219

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.  相似文献   

花岗岩残积土动态冲击性能的SHPB试验研究     

刘新宇  张先伟  岳好真  孔令伟  徐超 《岩土力学》2020,41(6):2001-2008

为研究花岗岩残积土的动态冲击性能，开展了高速冲击下的分离式霍普金森压杆（SHPB）试验，与常规应变率下的试验结果比较，分析了高应变率对花岗岩残积土的应力?应变特性和强度的影响。结果表明：低、高应变率下的花岗岩残积土的?-?a（轴向应力?轴向应变）曲线均呈现出软化型。随着应变率 增加，?-?a曲线向?a增大的方向移动，破坏应变?af增加。但高应变率下?af增加的程度更加明显。花岗岩残积土的峰值强度普遍具有应变率依赖性，二者可用直线关系拟合，但低、高应变率下的拟合关系并不一致。提出了率敏性因子m定量评价依赖性强弱。研究发现，随着应变率的提高，强度的应变率依赖性减弱，低应变率下的m为26.694，而高应变率下仅为0.013。相关试验结果指出，高速冲击荷载对土体总体有害。工程中应该采取合理措施控制冲击荷载的危害。该研究有助于深化花岗岩残积土动态冲击性能的理解，为相关工程的施工与设计提供技术参考。  相似文献   

江苏海岸中部近岸冬季潮汐和潮流特征     

王逸涵  王韫玮  于谦  蔡辉  高抒 《海洋科学》2019,43(10):66-74

南黄海西侧的江苏海岸近岸区域,素以地形复杂、潮流强劲、悬沙输运剧烈著称,但是较长期的同步潮位和潮流观测数据仍然缺乏,尤其是在近岸(20 km)浅水(20 m)区域。2014年1月在大丰港附近开展了连续潮位和潮流观测,获得的数据揭示了一系列特征。此地潮汐潮流为正规半日潮,浅水分潮显著。平均潮差为3.05 m,最显著的两个分潮为M2和S2分潮,振幅分别为1.45 m和0.52 m。潮流最显著的半日分潮M2分潮和最显著的浅水分潮M4分潮在沿岸方向上振幅分别为0.84m/s和0.12m/s,在跨岸方向上振幅分别为0.24 m/s和0.01 m/s,沿岸方向占绝对优势。潮波的沿岸传播介于前进波和驻波之间,驻波的特征稍强。M2分潮潮流椭圆最大流(长轴)方向为南偏东7.4°。存在冬季沿岸向北的余流,垂向平均值的大小为2.2 cm/s。以上潮汐潮流特征为该区域海洋物质输运研究提供了基础资料。  相似文献   

基于随机森林的光子计数激光雷达点云滤波     

陈博伟  庞勇  李增元  卢昊  梁晓军 《地球信息科学学报》2019,21(6):898-906

新一代星载激光雷达卫星ICESat-2首次采用了微脉冲光子计数激光雷达技术,由于单光子探测的灵敏性导致数据在大气和地表下层产生了大量噪声,因此对光子计数激光雷达点云数据实现信号和噪声的分离是开展进一步应用研究的前提和基础。本文选择美国俄勒冈州和弗吉尼亚州2个研究区,采用MATLAS数据,根据光子点云数据的特点构造了12个光子点云特征,对所构造的特征利用随机森林进行变量筛选,用机器学习方法对光子点云进行分类,并将建立好的模型推广到整个研究区。研究结果表明,本文构建的分类器分类总精度达到了96.79%,Kappa系数为0.94,平均生产者精度和用户精度分别为97.1%和96.8%。在相对弱噪声、平坦地形区域和强噪声、复杂地形区域都取得较好的分类结果。本文结果显示了基于少量样本通过机器学习的方法构建模型,可以推广到较大范围区域的光子点云分类应用中。  相似文献   

基于GIS的“一带一路”地区气温插值方法比较研究     

杨艳昭  郎婷婷  张超  贾琨 《地球信息科学学报》2020,22(4):867-876

“一带一路”倡议是新时期中国为加强对外开放提出的全球化合作倡议,资源环境的优化配置对全球化发展意义重大。气温作为重要的基础数据和输入要素,对其进行空间化处理是实现大尺度区域资源环境优化配置的前提。本文基于地理信息技术（GIS）,运用距离平方反比法（IDS）、协同克里格法（CK）、回归距离平方反比法（RIDS）和回归协同克里格法（RCK）,对“一带一路”地区1980—2017年的2679个气象站点的月平均气温和年平均气温数据进行插值,获得了“一带一路”地区10 km分辨率的气温空间分布数据。交叉验证结果表明：① IDS、CK、RIDS和RCK插值法在整体上均较好地展示了“一带一路”地区气温的地理空间分布规律,4种插值方法的月均气温的均方根误差分别在1.93~2.43、1.78~2.14、1.31~2.23和1.23~1.92 ℃之间;年均气温的均方根误差分别为1.94、1.83、1.37和1.27 ℃;② 在“一带一路”地区,加入协变量分析的CK插值精度整体优于IDS,并且削弱了IDS的极值现象;③ RIDS和RCK对年均气温的插值精度分别较IDS和CK提高了29.4%和30.6%,表明加入地理要素并进行残差修正的插值精度得到了进一步提高。总体来看,RCK插值法对气温数据的插值精度最高,可以考虑将此方法作为“一带一路”地区温度等气象要素的插值方法。  相似文献   

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

Peng Yue  Fan Gao  Boyi Shangguan  Zheren Yan 《International journal of geographical information science》2020,34(11):2243-2274

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.  相似文献   

A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes     

Yibin Ren  Huanfa Chen  Tao Cheng  Yang Zhang  Ge Chen 《International journal of geographical information science》2020,34(4):802-823

ABSTRACT

The spatio-temporal residual network (ST-ResNet) leverages the power of deep learning (DL) for predicting the volume of citywide spatio-temporal flows. However, this model, neglects the dynamic dependency of the input flows in the temporal dimension, which affects what spatio-temporal features may be captured in the result. This study introduces a long short-term memory (LSTM) neural network into the ST-ResNet to form a hybrid integrated-DL model to predict the volumes of citywide spatio-temporal flows (called HIDLST). The new model can dynamically learn the temporal dependency among flows via the feedback connection in the LSTM to improve accurate captures of spatio-temporal features in the flows. We test the HIDLST model by predicting the volumes of citywide taxi flows in Beijing, China. We tune the hyperparameters of the HIDLST model to optimize the prediction accuracy. A comparative study shows that the proposed model consistently outperforms ST-ResNet and several other typical DL-based models on prediction accuracy. Furthermore, we discuss the distribution of prediction errors and the contributions of the different spatio-temporal patterns.  相似文献   

Detecting regional dominant movement patterns in trajectory data with a convolutional neural network     

Can Yang  Győző Gidófalvi 《International journal of geographical information science》2020,34(5):996-1021

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