Automatic sub-pixel co-registration of Landsat-8 Operational Land Imager and Sentinel-2A Multi-Spectral Instrument images using phase correlation and machine learning based mapping     

《International Journal of Digital Earth》2013,6(12):1253-1269

ABSTRACT

This study investigates misregistration issues between Landsat-8/ Operational Land Imager and Sentinel-2A/ Multi-Spectral Instrument at 30?m resolution, and between multi-temporal Sentinel-2A images at 10?m resolution using a phase-correlation approach and multiple transformation functions. Co-registration of 45 Landsat-8 to Sentinel-2A pairs and 37 Sentinel-2A to Sentinel-2A pairs were analyzed. Phase correlation proved to be a robust approach that allowed us to identify hundreds and thousands of control points on images acquired more than 100 days apart. Overall, misregistration of up to 1.6 pixels at 30?m resolution between Landsat-8 and Sentinel-2A images, and 1.2 pixels and 2.8 pixels at 10?m resolution between multi-temporal Sentinel-2A images from the same and different orbits, respectively, were observed. The non-linear random forest regression used for constructing the mapping function showed best results in terms of root mean square error (RMSE), yielding an average RMSE error of 0.07?±?0.02 pixels at 30?m resolution, and 0.09?±?0.05 and 0.15?±?0.06 pixels at 10?m resolution for the same and adjacent Sentinel-2A orbits, respectively, for multiple tiles and multiple conditions. A simpler 1st order polynomial function (affine transformation) yielded RMSE of 0.08?±?0.02 pixels at 30?m resolution and 0.12?±?0.06 (same Sentinel-2A orbits) and 0.20?±?0.09 (adjacent orbits) pixels at 10?m resolution.  相似文献   

A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment     

Mousa Abedini  Bahareh Ghasemian  Ataollah Shirzadi  Himan Shahabi  Kamran Chapi  Binh Thai Pham 《国际地球制图》2013,28(13):1427-1457

Abstract

A novel artificial intelligence approach of Bayesian Logistic Regression (BLR) and its ensembles [Random Subspace (RS), Adaboost (AB), Multiboost (MB) and Bagging] was introduced for landslide susceptibility mapping in a part of Kamyaran city in Kurdistan Province, Iran. A spatial database was generated which includes a total of 60 landslide locations and a set of conditioning factors tested by the Information Gain Ratio technique. Performance of these models was evaluated using the area under the ROC curve (AUROC) and statistical index-based methods. Results showed that the hybrid ensemble models could significantly improve the performance of the base classifier of BLR (AUROC?=?0.930). However, RS model (AUROC?=?0.975) had the highest performance in comparison to other landslide ensemble models, followed by Bagging (AUROC?=?0.972), MB (AUROC?=?0.970) and AB (AUROC?=?0.957) models, respectively.  相似文献   

Elements of viral cartography     

Anthony C. Robinson 《制图学和地理信息科学》2013,40(4):293-310

ABSTRACT

Making and sharing maps is easier than ever, and social media platforms make it possible for maps to rapidly attain widespread visibility and engagement. Such maps can be considered examples of viral cartography – maps that reach rapid popularity via social media dissemination. In this research we propose a framework for evaluating the design and social dissemination characteristics of viral maps. We apply this framework in two case studies using maps that reached wide audiences on Twitter. We then analyze collections of maps derived from and inspired by viral maps using image analysis and machine learning to characterize their design elements. Based on our initial work to conceptualize and analyze virality in cartography, we propose a set of new research challenges to better understand viral mapmaking and leverage its social affordances.  相似文献   

Climatology of Shear Line and Related Rainstorm over the Southern Yangtze River Valley Based on an Improved Intelligent Identification Method     

刘金卿  陈鹤  徐靖宇 《热带气象学报(英文版)》2022,28(4):413-424

Based on four reanalysis datasets including CMA-RA, ERA5, ERA-Interim, and FNL, this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear. Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley (SYRV) during the summers (June-August) from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm, namely the t-distributed stochastic neighbor embedding method (t-SNE) and the k-means clustering method. The results are as follows: (1) The reproducibility of the 850 hPa wind fields over the SYRV using China’s reanalysis product CMA-RA is superior to that of European and American products including ERA5, ERA-Interim, and FNL. (2) Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities. (3) The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal, but the intensity and the accompanying rainstorm have a clear diurnal variation: they are significantly stronger during daytime than those at nighttime. (4) Half (47%) of the shear lines can cause short-duration rainstorms (≥20 mm (3h)-1 ), and shear line rainstorms account for one-sixth (16%) of the total summer short-duration rainstorms. Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing. (5) Under the influence of stronger water vapor transport and barotropic instability, shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south.  相似文献   

A Machine Learning-based Cloud Detection Algorithm for the Himawari-8 Spectral Image     

Chao LIU  Shu YANG  Di DI  Yuanjian YANG  Chen ZHOU  Xiuqing HU  Byung-Ju SOHN 《大气科学进展》2022,39(12):1994-2007

Cloud Masking is one of the most essential products for satellite remote sensing and downstream applications. This study develops machine learning-based (ML-based) cloud detection algorithms using spectral observations for the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. Collocated active observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used to provide reference labels for model development and validation. We introduce both daytime and nighttime algorithms that differ according to whether solar band observations are included, and the artificial neural network (ANN) and random forest (RF) techniques are adopted for comparison. To eliminate the influences of surface conditions on cloud detection, we introduce three models with different treatments of the surface. Instead of developing independent ML-based algorithms, we add surface variables in a binary way that enhances the ML-based algorithm accuracy by ~5%. Validated against CALIOP observations, we find that our daytime RF-based algorithm outperforms the AHI operational algorithm by improving the accuracy of cloudy pixel detection by ~5%, while at the same time, reducing misjudgment by ~3%. The nighttime model with only infrared observations is also slightly better than the AHI operational product but may tend to overestimate cloudy pixels. Overall, our ML-based algorithms can serve as a reliable method to provide cloud mask results for both daytime and nighttime AHI observations. We furthermore suggest treating the surface with a set of independent variables for future ML-based algorithm development.  相似文献   

楔刀作用下岩石微观劣化的试验研究   总被引：3，自引：0，他引：3  

莫振泽  李海波  周青春  邹飞  朱小明  王长柏  郝亚飞 《岩土力学》2012,33(5):1333-1340

通过改装RMT-150C系统,使用不同刃角的楔形刀具对花岗岩进行贯切试验,利用DISP声发射测试系统进行声发射的定位特征分析,研究岩石在楔形刀具作用下损伤劣化破坏的全过程。试验结果表明：声发射试验是一种研究TBM滚刀破岩微观机制的有效方法,脆性岩石材料的破坏过程是内部微裂纹的萌生和扩展过程的宏观反映;在楔刃刀具匀速贯入时,声发射信号随着荷载的变化呈一定对应趋势的变化,荷载-贯入度曲线能反映试件内的损伤历程;声发射2D定位结果显示,在岩石破碎区的密实核下方存在“损伤核”,它由微震裂源增生丛聚分布形成,核内劣化程度高,是孕育密实核的场所,为TBM滚刀作用下岩石的宏观破裂现象提供了解释和依据;楔刀跃进式的挤压贯切岩板试件,钝刃刀具产生“球状”损伤核,尖刃刀具产生“水滴状”损伤核,钝刃刀具贯切试件产生更深的贯入度和更宽的损伤核,致使试件中不仅破碎深度大,而且破损范围广,破岩效果优于尖刃刀具。  相似文献   

基于Sentinel-3 OLCI影像的巢湖藻蓝素浓度年内动态遥感监测     

汪志成  王杰  晏实江  崔玉环  王行行 《湖泊科学》2022,34(2):391-403

藻蓝素(PC)是水体蓝藻的指示性色素,其浓度反映了蓝藻生物量,利用卫星遥感监测藻蓝素浓度年内动态对蓝藻水华的有效防控有着重要意义.根据不同季节的巢湖藻蓝素浓度实测数据与同期Sentinel-3 OLCI影像,构建机器学习回归反演模型,应用于巢湖2019年OLCI影像集上,对巢湖藻蓝素浓度的空间分布、年内变化进行遥感监测...  相似文献   

基于变形信息分解的大坝变形趋势判断及预测研究     

郝永河  郝永艳  唐承忠  杨华 《大地测量与地球动力学》2021,41(8):841-845

在分离大坝变形数据信息的基础上,利用重标极差法实现大坝变形趋势判断,然后利用优化极限学习机及混沌理论实现大坝变形预测。重标极差分析表明,大坝变形始终具有正向持续性,但其程度具有减弱趋势。在变形预测过程中,模型参数的递进优化不仅能提高预测精度,还能有效提高其稳定性,预测模型的相对误差均值均小于2%,验证了本文预测思路的有效性。大坝变形趋势判断及预测结果一致性较好,均认为大坝变形仍会进一步增加,但增加幅度相对较小,趋向于稳定发展。  相似文献   

基于聚类算法的钻孔瞬变电磁视电阻率立体成像方法     

范涛  李鸿泰  刘磊  赵睿  李博凡  郭建磊  李宇腾 《地球科学与环境学报》2021,(2):343-355

煤矿井下地球物理水害超前探测要求探测点20 m范围内不得有积水和金属物体,传统电磁法超前探测技术已不能满足要求,钻孔瞬变电磁法通过将收发装置送入掘进工作面前方的钻孔中进行探测,既远离了巷道中的各种干扰,又提高了隐蔽致灾水体的探测精度。为解决该方法对钻孔径向异常体的准确定位解释难题,通过三维正演总结了其水平分量异常响应特征,提出了异常体象限确定准则,研究了根据水平分量幅值和异常象限综合求取异常体工具面角的计算方法。将由垂直分量计算得到的每一个视电阻率视为独立异常体,基于K-means聚类算法对相应的水平分量异常曲线特征值进行二分类,实现了全数据集的视电阻率象限自动划分,结合异常工具面角算法研究得出钻孔瞬变电磁视电阻率立体成像方法。最后计算了三维数值模型的立体成像结果,对钻孔径向的小规模低阻异常体取得了良好效果。结果表明:基于K-means聚类算法的钻孔瞬变电磁视电阻率立体成像方法是地球物理与机器学习的有机结合,该方法能够为井下掘进工作面隐伏水害超前探测精细解释提供技术支撑。  相似文献   

特征变量选择结合SVM的耕地土壤Hg含量高光谱反演   总被引：1，自引：0，他引：1  

郭云开  张思爱  王建军  章琼  谢晓峰 《测绘工程》2022,31(1):17-23

为探讨应用高光谱数据反演耕地土壤重金属汞(Hg)含量,对原始光谱进行10 nm重采样和SG平滑处理,用不同光谱变换数据与土壤重金属Hg含量进行相关性分析,采用IRIV、Random Frog和PCC提取光谱特征波段,分别建立SVM与GWO-SVM土壤Hg含量高光谱反演模型,获取Hg含量最优反演路径.研究表明,一阶微分变...  相似文献