基于高斯过程的高分辨率遥感图像变化检测     

陈克明  周志鑫  卢汉清  胡文龙  孙显 《遥感学报》2012,16(6):1192-1204

本文首先通过理论分析,探讨了高斯过程分类器在高分辨率遥感图像变化检测应用中的优势与不足,并针对高斯过程分类器的不足给出了相应的解决方法;其次,提出了一种基于空间上下文相关的高斯过程变化检测方法;最后,通过多个高分辨率遥感实验数据集上的实验设计与分析,验证了高斯过程分类器在高分辨率遥感图像变化检测中的应用能力,并证明了本文提出的解决方法的有效性.  相似文献   

支持向量机方法应用于理想时间序列的预测研究   总被引：3，自引：1，他引：2  

毛宇清  王咏青  王革丽 《气候与环境研究》2007,12(5):676-682

简要介绍了基于统计学习理论的支持向量机方法的基本思想和原理,利用该方法对33模Lorenz系统的理想混沌时间序列建立预测模型,并对在此基础上产生的非平稳时间序列进行预测试验研究。结果表明,支持向量机方法不仅对平稳过程有较好的预报能力,也可以适用于非平稳过程,对实际序列的预测有一定的启发意义。  相似文献   

基于克里格回归分析和机器学习算法的日本福岛县土地价格估算与制图的比较分析   总被引：1，自引：0，他引：1  

DERDOURlAhmed  村山佑司 《地理学报(英文版)》2020,30(5):794-822

Finding accurate methods for estimating and mapping land prices at the macro-scale based on publicly accessible and low-cost spatial data is an essential step in producing a meaningful reference for regional planners.This asset would assist them in making economically justified decisions in favor of key investors for development projects and post-disaster recovery efforts.Since 2005,the Ministry of Land,Infrastructure,and Transport of Japan has made land price data open to the public in the form of observations at dispersed locations.Although this data is useful,it does not provide complete information at every site for all market participants.Therefore,estimating and mapping land prices based on sound statistical theories is required.This paper presents a comparative study of spatial prediction of land prices in 2015 in Fukushima prefecture based on geostatistical methods and machine learning algorithms.Land use,elevation,and socioeconomic factors,including population density and distance to railway stations,were used for modeling.Results show the superiority of the random forest algorithm.Overall,land prices are distributed unevenly across the prefecture with the most expensive land located in the western region characterized by flat topography and the availability of well-connected and highly dense economic hotspots.  相似文献   

基于规则知识表示的模型单元选择专家系统的实现   总被引：5，自引：0，他引：5       下载免费PDF全文

李新中  赵鹏大 《地球科学》1995,20(2):173-178

本文根据矿床统计预测中模型单元选择的领域专家知识，用基于规则知识表示的ＧＵＲＵ专家系统工具及Ｃ语言建造了一个模型单元选择的专家系统及其咨询界面，解决了如何确定最佳的模型单元的这一难题，最终形成了模型单元数据库，这就提高了数学模型的精度和预测的效果，为用户提供了一个高效率的计算机辅助工具。  相似文献   

静压扩底灌注桩在工程中的应用          下载免费PDF全文

熊冬法  陈大路  谢晋仁 《探矿工程》1995,(3):5-7

根据温岭太平镇的地质情况，采用了桩长为４．０￣５．８ｍ间的静压扩底灌注短桩进行施工，并对该种桩型的适用性和推广价值作了探索。  相似文献   

Air pollution hazard assessment using decision tree algorithms and bivariate probability cluster polar function: evaluating inter-correlation clusters of PM10 and other air pollutants     

Omar F. Althuwaynee  Wesam Al Madhoun 《地理信息系统科学与遥感》2020,57(2):207-226

ABSTRACT

The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient monitoring data around the study area. Two air quality stations, CA0016 and CA0054, were selected for this research due to their strategic locations. Non-linear RPart and Tree model of Decision Tree (DT) algorithm within the R programming environment were adopted for classification analysis. The pollutants’ respective significance to PM10 occurrence was evaluated using Random forest (RF) of DT algorithms and K means polar cluster function identified and grouped similar features, and also detected vital clusters in ambient monitoring data around the industrial areas. Results show increase in the number of clusters did not significantly alter results. PM10 generally shows a reduction in trend, especially in SW direction and an overall minimal reduction in the pollutants’ concentration in all directions is observed (less than 1). Fluctuations were observed in the behaviors of CO and NO_x during the day while NOx displayed relative stability. Results also show that a direct and positive linear relationship exists between the PM10 (target pollutant) and CO, SO₂, which suggests that these pollutants originate from the same sources. A semi-linear relationship is observed between the PM10 and others (O₃ and NO_x) while humidity shows a negative linearity with PM10. We conclude that most of the major pollutants show a positive trend toward the industrial areas in both stations while tra?c emissions dominate this site (CA0016) for CO and NOx. Potential applications of nuggets of information derived from these results in reducing air pollution and ensuring sustainability within the city are also discussed. Results from this study are expected to provide valuable information to decision makers to implement viable strategies capable of mitigating air pollution effects.  相似文献   

Multi-source spatial data-based invasion risk modeling of Striga (Striga asiatica) in Zimbabwe     

Bester Tawona Mudereri  Elfatih Mohamed Abdel-Rahman  Timothy Dube  Tobias Landmann  Zeyaur Khan  Emily Kimathi 《地理信息系统科学与遥感》2020,57(4):553-571

ABSTRACT

Monitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially vulnerable cropping areas. Vegetation phenology from remote sensing, bioclimatic and other environmental variables (i.e. cropping system, edaphic, land surface temperature, and terrain) were used as predictors. Six machine learning modeling techniques and the ensemble model were evaluated on their suitability to predict current and future Striga weed distributional patterns. The mentioned predictors (n = 40) were integrated into six models with “presence-only” training and evaluation data, collected in Zimbabwe over the period between the 12^th and 28^th of March 2018. The area under the curve (AUC) and true skill statistic (TSS) were used to measure the performance of the Striga modeling framework. The results showed that the ensemble model had the strongest Striga occurrence predictive power (AUC = 0.98; TSS = 0.93) when compared to the other modeling algorithms. Temperature seasonality (Bio4), the maximum temperature of the warmest month (Bio5) and precipitation seasonality (Bio15) were determined to be the most dominant bioclimatic variables influencing Striga occurrence. “Start of the season” and “season minimum value” of the “Enhanced Vegetation Index base value” were the most relevant remote sensing-based variables. Based on projected climate change scenarios, the study showed that up to 2050, the suitable area for Striga propagation will increase by ~ 0.73% in Zimbabwe. The present work demonstrated the importance of integrating multi-source data in predicting possible crop production restraints due to weed propagation. The results can enhance national preparedness and management strategies, specifically, if the current and future risk areas can be identified for early intervention and containment  相似文献   

Increasing the detail of European land use/cover data by combining heterogeneous data sets     

Konštantín Rosina  Filipe Batista e Silva  Pilar Vizcaino  Mario Marín Herrera  Sérgio Freire  Marcello Schiavina 《International Journal of Digital Earth》2020,13(5):602-626

ABSTRACT

Data on land use and land cover (LULC) are a vital input for policy-relevant research, such as modelling of the human population, socioeconomic activities, transportation, environment, and their interactions. In Europe, CORINE Land Cover has been the only data set covering the entire continent consistently, but with rather limited spatial detail. Other data sets have provided much better detail, but either have covered only a fraction of Europe (e.g. Urban Atlas) or have been thematically restricted (e.g. Copernicus High Resolution Layers). In this study, we processed and combined diverse LULC data to create a harmonised, ready-to-use map covering 41 countries. By doing so, we increased the spatial detail (from 25 to one hectare) and the thematic detail (by seven additional LULC classes) compared to the CORINE Land Cover. Importantly, we decomposed the class ‘Industrial and commercial units’ into ‘Production facilities’, ‘Commercial/service facilities’ and ‘Public facilities’ using machine learning to exploit a large database of points of interest. The overall accuracy of this thematic breakdown was 74%, despite the confusion between the production and commercial land uses, often attributable to noisy training data or mixed land uses. Lessons learnt from this exercise are discussed, and further research direction is proposed.  相似文献   

基于Sentinel-1A数据的多种机器学习算法识别冰山的比较   总被引：1，自引：0，他引：1  

肖湘文  沈校熠  柯长青  周兴华 《测绘学报》2020,49(4):509-521

冰山识别对于海洋环境监测和船只安全运行等具有重要的意义,是北极航道开通和北极开发过程中的重要内容。采用合成孔径雷达(SAR)影像进行冰山识别具有独特的优势,多种机器学习算法均可用于SAR影像的冰山识别中。为了最大限度地发挥机器学习算法的性能,有必要对不同机器学习算法及其搭配使用的特征与特征标准化方法进行评估,从而进行最优冰山识别方法的选择。因此,本文基于Sentinel-1A SAR影像,采用多种机器学习方法、多种特征组合及多种特征标准化方法进行冰山识别,并比较各流程方法的识别性能差异。采用的机器学习算法包括贝叶斯分类器(Bayes)、反向神经网络(BPNN)、线性判别分析(LDA)、随机森林(RF)以及支持向量机(SVM);特征标准化方法包括Min-max标准化、Z-score标准化及log函数标准化;数据集是含有12个SAR影像特征的969个冰山与非冰山样本,样本主要位于格陵兰岛东海岸。分类效果采用接收者操作特性(ROC)曲线下的面积(AUC)进行衡量。结果显示,最佳搭配下的RF的AUC值最高,达到了0.945,比最差的Bayes高出0.09。从识别率上来看,RF在冰山查全率为80%的情况下非冰山查全率达到92.6%,效果最好,比第2位的BPNN高出1.4%,比最差的Bayes高出2.6%;BPNN在冰山查全率为90%的情况下非冰山查全率达到87.4%,比第2位的RF高出0.8%,比最差的Bayes高出2.7%。上述结果表明,对冰山识别而言,选择最优的机器学习算法和最佳的特征与特征标准化方法都是十分重要的。  相似文献   

Evaluation of the parameters affecting the roughness coefficient of sewer pipes with rigid and loose boundary conditions via kernel based approaches     

《国际泥沙研究》2020,35(2):171-179

One of the important issues in water transport and sewer systems is determining the flow resistance and roughness coefficient.An accurate estimation of the roughness coefficient is a substantial issue in the design and operation of hydraulic structures such as sewer pipes,the calculation of water depth and flow velocity,and the accurate characterization of energy losses.The current study,applies two kernel based approaches [Support Vector Machine(SVM) and Gaussian Process Regression(GPR)] to develop roughness coefficient models for sewer pipes.In the modeling process,two types of sewer bed conditions were considered:loose bed and rigid bed.In order to develop the models,different input combinations were considered under three scenarios(Scenario 1:based on hydraulic characteristics,Scenarios2 and 3:based on hydraulic and sediment characteristics with and without considering sediment concentration as input).The results proved the capability of the kernel based approaches in prediction of the roughness coefficient and it was found that for prediction of this parameter in sewer pipes Scenario 3 performed better than Scenarios 1 and 2.Also,the sensitivity analysis results showed that Dgr(Dimensionless particle number) for a rigid bed and w_b/y(ratio of deposited bed width,w_b,to flow depth,y) for a loose bed had the most significant impact on the modeling process.  相似文献