整合GIS的生态环境建模与EDSS研究进展

围绕生态环境的空间问题建模及模型集成机制,综合评述了环境建模及其应用软件的研究现状和发展趋势,对比分析了生态环境模型与GIS集成的多种方式,重点讨论了当前环境决策支持系统的开发策略和研究热点,并结合本体技术和语义网服务展望了未来环境决策支持系统解决方案的特征。     

中国滑坡降雨试验的研究现状与发展趋势

滑坡是世界上分布最广、危害最严重的地质灾害类型。降雨等因素作用下滑坡沿着滑动面发生剪切大变形,造成抗滑结构发生剪切破坏,然而目前抗滑结构设计未能充分考虑滑坡剪切大变形的静态、刚性设计,亟需开展滑坡-韧性锚固体系演化机理的深入研究。本文基于塑性变形理论提出了一种韧性锚固结构,建立了其力学模型,并以开挖后种蜂场滑坡为原型,设计了模型试验,对比分析了有无锚杆加固下边坡体变形、土压力等多源数据变化规律,结果发现：(1)韧性锚杆加固作用下边坡内测点侧向应力分布与曲线形态发生显著变化,边坡抗滑力增大了20%。(2)边坡剪切大变形过程中,韧性锚杆轴力呈周期性变化,与理论相符。(3)韧性锚杆明显减少了边坡位移及变形破坏范围,滑动范围减少了约25%。     

基于SVM的含约束地球化学单元素异常圈定方法及应用

地球化学元素异常的空间分布受限于地质背景、矿产空间分布和景观特征等因素,单元素异常图的绘制不应仅依据采样数据由计算机追踪等值线图的方法自动勾绘。传统的手工圈定方法效率低、受人为因素影响大。基于SVM(Support Vector Machine,支持向量机)提出了一种可以考虑地质约束的单元素异常圈定方法,并加入地质单元分区、地质构造等约束条件。该方法可以高效绘制出单元素异常图,并成功应用于2个实例中。     

基于SVM的资源三号测绘卫星影像分类

以江苏省宜兴市为研究区,利用支持向量机(SVM)方法对资源三号测绘卫星影像进行了分类,其总分类精度为97.76%,Kappa精度为0.968 7。为了评价算法的适用性,同时应用最大似然法与最小距离法对同一影像进行分类测试,支持向量机分类法精度高于其他2种方法,可以满足土地覆盖分类调查需求。     

Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs

This article employs Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of Evaporation Losses (E) in reservoirs. SVM that is firmly based on the theory of statistical learning theory, uses regression technique by introducing ε‐insensitive loss function has been adopted. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The input of SVM and RVM models are mean air temperature (T) ( °C), average wind speed (WS) (m/sec), sunshine hours (SH)(hrs/day), and mean relative humidity (RH) (%). Equations have been also developed for prediction of E. The developed RVM model gives variance of the predicted E. A comparative study has also been presented between SVM, RVM and ANN models. The results indicate that the developed SVM and RVM can be used as a practical tool for prediction of E. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting the ionospheric f0F2 parameter one hour ahead using a support vector machine technique

This paper proposes a method for forecasting the ionospheric critical frequency, f₀F₂, 1 h in advance, using the support vector machine (SVM) approach. The inputs to the SVM network are the time of day, seasonal information, 2 month running mean sunspot number (R2), 3 day running mean of the 3 h planetary magnetic ap index, the solar zenith angle, the present value f₀F₂(t) and its first and second increments, the observation of f₀F₂ at t?23 h, the 30-day mean value at time, t, f_mF₂ (t) and the previous 30 day running mean of f₀F₂ at t?23 h f_mF₂(t?23). The output is the predicted f₀F₂ 1 h ahead. The network is trained to use the ionospheric sounding data at Haikou, Guangzhou, Chongqing, Lanzhou, Beijing, Changchun and Manzhouli stations at high and low solar activities. The performance of the SVM model was verified with observed data. It is shown that the predicted f₀F₂ has good agreement with the observed f₀F₂. The performance of the SVM model is superior to that of the autocorrelation and persistence models, and that it is comparable to that of the neural network model.     

Discrimination of crop types with TerraSAR-X-derived information

Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J–M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5–96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications.     

多核学习算法及其在高光谱图像分类中的应用研究进展

多核学习算法在高光谱图像分类领域占据着十分重要的地位。与灰度图像、全色图像和多光谱图像等相比,高光谱图像因具有很强的分类识别能力等多方面优势而被广泛应用。为进一步提高高光谱图像的分类精度,促进多核学习算法在高光谱图像分类中的应用,本文对多核学习算法及其在高光谱图像分类中的应用进行了总结。首先在回顾核方法的基础上阐述了多核学习框架,其次对多核学习核函数组合方法进行综述,随后根据求解多核学习组合系数方法的不同将多核学习分为两类：固定规则的多核学习算法和基于优化的多核学习算法,并对两类多核学习算法在高光谱图像分类中的应用进行综述,总结各类算法在高光谱图像分类的应用进展。同时,为了便于研究者对多核学习算法及其在高光谱图像分类问题中的应用研究,本文对常用核函数和高光谱图像数据集进行了整理归纳。最后,讨论了多核学习算法在高光谱图像分类研究方面的不足,并对未来研究方向进行了展望,以期为该领域的研究和应用提供参考。     

利用风廓线雷达资料分析大理机场飑线天气过程的风垂直变化特征

飑线天气现象是航空气象中危险和复杂的天气现象之一,2018年大理机场出现两次飑线天气过程,利用风廓线雷达资料和航空气象地面观测资料,分析两次飑线天气过程的风垂直变化特征。结果表明:(1)在飑线天气过程开始前2～4 h,从底层到高层均出现明显的上升运动,飑现象开始前后,上升运动和下沉运动同时存在,为大气中垂直热交换过程提供了有利条件;(2)飑线天气过程存在垂直风的水平切变及垂直切变,反映了强烈的对流发展,发展到离地高度5000 m以上;(3)飑线天气过程存在径向速度突变增大、谱宽变宽和单波束径向速度折叠现象,说明本场出现的是风雨交加的强对流天气;(4)高空到地面存在湍流运动,能量交换频繁,气流紊乱。     

Supervised change detection in VHR images using contextual information and support vector machines

In this paper we study an effective solution to deal with supervised change detection in very high geometrical resolution (VHR) images. High within-class variance as well as low between-class variance that characterize this kind of imagery make the detection and classification of ground cover transitions a difficult task. In order to achieve high detection accuracy, we propose the inclusion of spatial and contextual information issued from local textural statistics and mathematical morphology. To perform change detection, two architectures, initially developed for medium resolution images, are adapted for VHR: Direct Multi-date Classification and Difference Image Analysis. To cope with the high intra-class variability, we adopted a nonlinear classifier: the Support Vector Machines (SVM). The proposed approaches are successfully evaluated on two series of pansharpened QuickBird images.