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   

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   

成矿预测:从二维到三维   总被引：1，自引：0，他引：1  

袁峰  张明明  李晓晖  葛粲  陆三明  李建设  周宇章  兰学毅 《岩石学报》2019,35(12):3863-3874

随着矿产资源勘探方法以及计算机科学技术的不断发展,成矿预测的理论和方法已从定性发展至定量,从二维拓展到三维。近十年来,随着深部矿产资源勘探工作的推进,三维成矿预测研究得到了迅猛发展,相关理论与方法也已逐步走向成熟。本文总结了国内外二维成矿预测研究的现状,同时对近十年来国内外学者在三维地质建模技术、三维成矿预测方法等方面的主要成果和进展做了系统总结和分析。目前,国内外多个地区已相继开展了三维成矿预测工作,并成功圈定多个深部找矿靶区,相关成果为深部找矿勘探工作提供了新的方法和方向。在此基础上,本文对未来三维成矿预测的发展趋势进行展望,相较于传统的二维成矿预测,三维成矿预测往往受限于三维预测信息的缺乏。如何更好的挖掘二维数据在深度方向的指示能力,将二维数据推演至三维环境,利用数值模拟、机器学习等方法开展数据挖掘、充分发挥已有数据的内蕴信息将在未来推动三维成矿预测理论的深入发展,提高三维成矿预测的理论方法及应用实践水平。  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   

1992年以来盐城滨海湿地植被动态变化研究     

张佳佳  沈永明 《海洋科学》2018,42(8):14-21

基于1992～2016年4个时相的遥感数据, 对盐城滨海川东港至梁垛河口岸段进行景观分类, 分析湿地植被类型及分布, 借助转移矩阵法探究滨海湿地植被的时空演变。结果表明: 茅草、芦苇、碱蓬, 米草为川东港至梁垛河口段主要湿地植被类型。1992～2016年植被总面积减少, 其中茅草几近消失殆尽; 芦苇、碱蓬面积持续缩减; 米草面积大幅增长。1992～2000年植被面积快速缩减, 大面积的茅草碱蓬地被开发为耕地; 2000～2009年养殖塘侵占湿地植被, 规模剧增。2009～2016年, 米草向海方向扩张的同时靠陆侧转变为养殖塘。川东港至梁垛河口的植被演变是自然和人为共同作用的结果。一方面受淤积型海岸影响, 另一方面受政策引导下的各类滩涂开发活动, 及互花米草的引种所带来的快速扩散的影响。本文研究结果为盐城滨海湿地管理与可持续发展提供理论参考。  相似文献   

A model‐based data‐driven dictionary learning for seismic data representation          下载免费PDF全文

Can Evren Yarman  Rajiv Kumar  James Rickett 《Geophysical Prospecting》2018,66(1):98-123

Planar waves events recorded in a seismic array can be represented as lines in the Fourier domain. However, in the real world, seismic events usually have curvature or amplitude variability, which means that their Fourier transforms are no longer strictly linear but rather occupy conic regions of the Fourier domain that are narrow at low frequencies but broaden at high frequencies where the effect of curvature becomes more pronounced. One can consider these regions as localised “signal cones”. In this work, we consider a space–time variable signal cone to model the seismic data. The variability of the signal cone is obtained through scaling, slanting, and translation of the kernel for cone‐limited (C‐limited) functions (functions whose Fourier transform lives within a cone) or C‐Gaussian function (a multivariate function whose Fourier transform decays exponentially with respect to slowness and frequency), which constitutes our dictionary. We find a discrete number of scaling, slanting, and translation parameters from a continuum by optimally matching the data. This is a non‐linear optimisation problem, which we address by a fixed‐point method that utilises a variable projection method with ?₁ constraints on the linear parameters and bound constraints on the non‐linear parameters. We observe that slow decay and oscillatory behaviour of the kernel for C‐limited functions constitute bottlenecks for the optimisation problem, which we partially overcome by the C‐Gaussian function. We demonstrate our method through an interpolation example. We present the interpolation result using the estimated parameters obtained from the proposed method and compare it with those obtained using sparsity‐promoting curvelet decomposition, matching pursuit Fourier interpolation, and sparsity‐promoting plane‐wave decomposition methods.  相似文献   

地方政府土地出让干预对区域工业碳排放影响的对比分析——以中国8大经济区为例     

王博  吴天航  冯淑怡 《地理科学进展》2020,39(9):1436-1446

探讨不同区域地方政府干预对碳排放的影响差异,对于中国推进碳减排战略、协调区域经济社会发展具有重要意义。鉴于此,论文基于地方政府土地出让的视阈,以中国8大经济区为研究对象,有机耦合STIRPAT模型与CKC模型,构建形成STIRPAT拓展模型,并使用2007—2016年中国28个省(市、自治区)的工业面板数据,对比考察8大经济区政府土地出让干预对区域碳排放的影响差异。结果表明:不同经济区的地方政府土地出让干预对区域碳排放的影响存在显著差异。其中,同为CKC“倒N”型的北部沿海、南部沿海、长江中游经济区,其政府土地出让干预对区域碳排放的影响呈现出北部沿海经济区为负、南部沿海经济区为正、长江中游经济区无显著影响的差异效果;同为CKC“倒U”型的东北、西南和西北经济区,其政府土地出让干预对区域碳排放的影响,东北和西南经济区都显著为负,西北经济区未表现出显著影响;同为CKC“U”型的东部沿海和黄河中游经济区,其政府土地出让干预对碳排放也呈现出前者为负、后者为正的相反影响。研究结果可为制定碳减排差别化政策、协调区域可持续发展提供参考。  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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