An efficient unsupervised index based approach for mapping urban vegetation from IKONOS imagery     

《International Journal of Applied Earth Observation and Geoinformation》2016

Despite the increased availability of high resolution satellite image data, their operational use for mapping urban land cover in Sub-Saharan Africa continues to be limited by lack of computational resources and technical expertise. As such, there is need for simple and efficient image classification techniques. Using Bamenda in North West Cameroon as a test case, we investigated two completely unsupervised pixel based approaches to extract tree/shrub (TS) and ground vegetation (GV) cover from an IKONOS derived soil adjusted vegetation index. These included: (1) a simple Jenks Natural Breaks classification and (2) a two-step technique that combined the Jenks algorithm with agglomerative hierarchical clustering. Both techniques were compared with each other and with a non-linear support vector machine (SVM) for classification performance. While overall classification accuracy was generally high for all techniques (>90%), One-Way Analysis of Variance tests revealed the two step technique to outperform the simple Jenks classification in terms of predicting the GV class. It also outperformed the SVM in predicting the TS class. We conclude that the unsupervised methods are technically as good and practically superior for efficient urban vegetation mapping in budget and technically constrained regions such as Sub-Saharan Africa.  相似文献   

使用小型真空包装机密封快速测量地质样品小体重     

任金鑫  班俊生  张燕婕  刘桂珍  杨莹雪  郑寅 《矿产与地质》2016,(1):147-150

文章介绍了一种使用小型真空包装机密封试样,进而使静水力学挂钩电子天平快速测量地质样品小体重的方法。实验表明:使用同批次真空袋包装测量物理特性不同的地质样品(致密坚硬的石英岩、铝土矿、磁铁矿、铅锌矿、铝块以及具有吸水性的大孔隙率矿石)的小体重,其结果准确可靠,与传统蜡封法的测量结果一致;同批次真空包装袋密度测量值的相对标准偏差(RSD)仅为0.08%~0.10%,因此在实际测试中只需对同批次真空袋质量和密度进行一次测量,同时称量样品质量及其真空包装密封后在水中的质量,便可计算出地质样品的小体重。真空包装密封法相比于传统蜡封法,成本低且操作简便,尤其是测量吸水性地质样品的小体重时,采取真空密封处理能更好地保持矿石的原始状态,可以省去样品含水率测量及相关的校正过程;而且使用静水力学挂钩电子天平称量样品在水中的质量相比于使用普通电子天平称量样品排开水的质量,测量结果也更加精确,该方法值得在岩矿分析实验室应用及推广。  相似文献   

熵权遗传算法及极限学习机地铁隧道沉降预测     

高彩云  崔希民  高宁 《测绘科学》2016,41(2):71-75

针对目前的地铁隧道沉降变形预测方法忽略了对沉降变形影响因素的综合协调考虑这一问题,该文将遗传算法(GA)结合极限学习机(ELM)的方法引入地铁隧道沉降变形预测。该方法借助最大信息熵理论,充分挖掘地铁隧道沉降主要影响因素与沉降量间的信息特征,并将遗传算法与极限学习机相耦合,利用遗传算法的全局搜索能力获取ELM神经网络优化的初始权值和阈值,形成熵权遗传算法-极限学习机模型,并编制相应计算程序。采用该模型对西安某地铁隧道沉降变形进行预测,并与遗传算法-极限学习机、极限学习机、传统的BP神经网络预测结果进行比较,结果表明熵权遗传算法-极限学习机模型与实测值吻合更好,预测结果更稳定。  相似文献   

TGQ背包式取样钻机的研制          下载免费PDF全文

冉灵杰  宋殿兰  卢猛 《探矿工程》2016,43(6):49-51，55

目前在普查踏勘阶段,地质工作者要求随时随地取样,迫切地需要迁移便捷、快速钻进、高效高质的便携式取样钻机。本文介绍了研制的TGQ背包式取样钻机的参数、特点及试验情况。该钻机体积小、质量轻、结构设计合理,具有高、低两挡转速,可以实现金刚石钻进、螺旋钻进工艺,可以进行斜孔钻进取心,可以解决复杂地层的取样问题。钻具采用高强度的材料,钻杆采用插装式快速连接结构,提高了加减钻杆的效率。钻机的研制打破了国外背包式轻便钻机对我国市场的垄断。  相似文献   

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning     

Nian LIU  Zhongwei YAN  Xuan TONG  Jiang JIANG  Haochen LI  Jiangjiang XIA  Xiao LOU  Rui REN  Yi FANG 《大气科学进展》2022,39(10):1721-1733

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields, i.e., to reconstruct the surface wind speed at any location, based on meteorological background fields and geographical information. The random forest method is selected to develop the machine learning data reconstruction model (MLDRM-RF) for wind speeds over Beijing from 2015–19. We use temporal, geospatial attribute and meteorological background field features as inputs. The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance. The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error (RMSE) of the reconstructed wind speed field across Beijing. The average RMSE is 1.09 m s^?1, considerably smaller than the result (1.29 m s^?1) obtained with inverse distance weighting (IDW) interpolation. Finally, we extract the important feature permutations by the method of mean decrease in impurity (MDI) and discuss the reasonableness of the model prediction results. MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions. Such a model is needed in many wind applications, such as wind energy and aviation safety assessments.  相似文献   

PCA和布谷鸟算法优化SVM的遥感矿化蚀变信息提取   总被引：1，自引：1，他引：0  

吴一全  盛东慧  周杨 《遥感学报》2018,22(5):810-821

为了进一步提高遥感矿化蚀变信息提取的精度,本文提出了一种基于主成分分析PCA (Principal Component Analysis)和布谷鸟算法优化支持向量机SVM (Support Vector Machine)的遥感矿化蚀变信息提取方法。首先,通过波段比值法增强研究区遥感图像中的矿化蚀变信息,并获得比值图像;然后,对比值图像进行主成分分析,进而提取训练样本;接着,利用SVM对训练样本进行训练,同时采用布谷鸟算法求取SVM的最优核参数及惩罚因子,构造最优SVM模型;最后,运用最优SVM模型完成矿化蚀变信息提取。选择青海省五龙沟地区为研究区,提取羟基及铁染蚀变信息。实验结果表明,与主成分分析法、基于光谱角法和SVM的方法、基于粒子群和SVM的方法及基于波段比值、PCA和粒子群优化SVM的方法等4种方法相比,本文方法获得的遥感矿化蚀变信息和已知矿点的吻合度最高,提取效果最好。  相似文献   

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants     

Solomon G. Tesfamichael  Solomon W. Newete  Elhadi Adam  Bambo Dubula 《地理信息系统科学与遥感》2018,55(3):417-436

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.  相似文献   

TBM法用于金属矿山深部开拓巷道的风险分析及应对措施          下载免费PDF全文

朱爱山  周慧鹏  李勇 《探矿工程》2021,48(7):110-114

国内金属矿山深部采矿,采用钻爆法工程工期很长,不确定的各项风险较大。TBM法施工速度快、安全、质量好,在水利、市政、公路等运用已很广泛。“四新技术”的不断应用,设备的不断改进,国产TBM性能和质量不断提升,施工成本也大幅降低。但是TBM法开拓深部采矿巷道还没有应用,主要原因是矿山深部地质情况复杂,巷道断面不一,TBM适应性差。本文分析了TBM法用于金属矿山深部巷道开拓可能遇到的涌水、岩爆、高地温等风险,并对这些风险应对措施作了阐述。认为采取措施后可以消除这些风险,TBM机可以在金属矿山深部巷道中试验应用。  相似文献   

Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine     

SUN Feng  HU Haotian  ZHAO F  YANG Xinran  CHEN Zubin  WU Haidong  ZHANG Linyou 《《地质学报》英文版》2021,95(6):1940-1947

Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine (GRU_SVM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine (SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average (STA/LTA) method with GRU_SVM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.  相似文献   

Development of a support‐vector‐machine‐based model for daily pan evaporation estimation     

Gwo‐Fong Lin  Hsuan‐Yu Lin  Ming‐Chang Wu 《水文研究》2013,27(22):3115-3127

Evaporation estimation is an important issue in water resources management. In this article, a four‐season model with optimal input combination is proposed to estimate the daily evaporation. First, the model based on support vector machine (SVM) coupled with an input determination process is used to determine the optimal combination of input variables. Second, a comparison of the SVM‐based model with the model based on back‐propagation network (BPN) is made to demonstrate the superiority of the SVM‐based model. In addition, season data are used to construct the SVM‐based four‐season model to further improve the daily evaporation estimation. An application is conducted to demonstrate the performance of the proposed model. Results show that the SVM‐based model can select the optimal input combination with physical mechanism. The SVM‐based model is more appropriate than the BPN‐based model because of its higher accuracy, robustness and efficiency. Moreover, the improvement due to the use of the four‐season model increases from 3.22% to 15.30% for RMSE and from 4.84% to 91.16% for CE, respectively. In conclusion, the SVM‐based model coupled with the proposed input determination process should be used to select input variables. The proposed four‐season SVM‐based model with optimal input combination is recommended as an alternative to the existing models. The proposed modelling technique is expected to be useful to improve the daily evaporation estimation. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献