专家主持气象节目的优势与问题分析

目前专家主持播讲的电视节目正在悄然兴起,而在电视气象节目中专家型主持人非常少见。本文探讨了专家主持播讲气象节目的优势、可行性以及应注意的问题,对专家主持播讲气象节目的形式给予了充分肯定,同时指出必须切实避免一些容易出现的问题。     

乌鲁木齐二电厂断裂地震工程地质效应研究

强烈构造活动区重大工程场址的评价是目前国内外研究的难题。本文以新疆乌鲁木齐二电厂为例,应用风险度和专家系统对该厂区及附近断裂的活动性及其地震工程地质交效应进行了研究。采用Ｗｅｉｂｕｌｌ函数,综合考虑了裂活动可能产生的振动、错动,蠕滑,地面裂缝,液化和崩塌等６种因素,建立了该区断裂地震工程地质模型;运用ＣＲＵＳＴＡＢ专家系统,综合了地质条件,地震条件和地震球理对厂区断断裂可能产生的地震地面运动,断及     

钻进过程实时状态监控与事故诊断专家系统——人工智能在钻井工程中的应用之一

提出了钻进过程实时状态监控与事故诊断的人工智能方法—专家系统,给出了实现钻进过程实时状态监控与事故诊断专家系统的总体思想,以及如何建立综合数据库、产生式规则和控制策略的具体方法和步骤。试验证明这种方法切实可行,具有一定的应用价值     

专家系统和神经网络相结合用于钻进过程安全监控

提出了把专家系统和神经网络混合起来使用的新方法, 这种方法可以克服专家系统和神经网络单独使用时的缺陷。以钻进过程的安全监控为例, 研究了这种方法在确定性和不确定性条件下的使用步骤及效果。     

结合Freeman分解与子孔径散射特性的极化SAR图像分类

本文结合Freeman分解和子孔径分析,提出一种新的极化SAR图像分类算法。该方法首先利用子孔径分解,产生不同方位观察角度下的子孔径图像,再利用Freeman分解对各个子孔径图像提取三种散射机理成分的功率,平均后对类别进行细分,最后使用Wishart统计分类器对类别进行分类划分得到最终结果。该方法考虑了极化散射机理在不同方位观察角度下的变化,能够取得较好的分类效果,能够保存主要极化散射特性的纯度,同时还可以动态地设定分类类别数。最后利用EMISAR获取的极化SAR数据进行了仿真,验证了该方法的有效性。     

基于多光谱LiDAR数据的道路中心线提取

针对城市三维激光点云中,道路与地面高程相差小、激光反射强度相近使得道路提取困难;广场、停车场等地物的高程、反射强度与道路极为相近,容易产生错误提取的问题。本文设计了一种描述道路条带信息的局部二进制特征（Stripe Local Binary Feature, SLBF）,结合LiDAR数据中的三维信息和多光谱信息获得基于强度、密度和平坦度等统计特征（Statistics-Based Feature, SBF）,并采用随机森林分类器实现了机载点云中道路面点云和非道路面点云的有效提取。通过欧式聚类精化道路点云和迭代腐蚀边界细化中心线,进而获得矢量化的道路中心线。以Waddenzee区域的多光谱机载点云数据进行实验验证,道路中心线提取结果的完整度达到94.15%,准确度达到97.95%,精度达到92.28%。实验结果表明,该方法可以有效地提取道路中心线,同时由于设计的特征具有不变性,能够适用于城市和林间小路等各种环境。     

高光谱影像分类及组合分类器的应用

高光谱影像包含有丰富的地物类别信息,但由于受分类方法等因素的限制,这些信息并不能得到充分的挖掘。组合分类器由于可以实现各分类结果信息的互补,因而总有着较各单分类器效果更好的分类性能。将组合分类方法引入到高光谱影像的分类实践中,并对组合的关键问题一组合规则进行探讨,提出了混合组合规则的概念。实验表明,利用该组合分类方法对高光谱影像进行分类,可以得到良好的处理结果。     

Improving the Wishart Synthetic Aperture Radar image classifications through Deterministic Simulated Annealing

This paper proposes the use of Deterministic Simulated Annealing (DSA) for Synthetic Aperture Radar (SAR) image classification for cluster refinement. We use the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution that is then supplied to the DSA optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The improvement is verified by computing a cluster separability coefficient. During the DSA optimization process, for each iteration and for each pixel, two consistency coefficients are computed taking into account two kinds of relations between the pixel under consideration and its neighbors. Based on these coefficients and on the information coming from the pixel itself, it is re-classified. Several experiments are carried out to verify that the proposed approach outperforms the Wishart strategy. We try to improve the classification results by considering the spatial influences received by a pixel through its neighbors. Finally, a link about the contribution of DSA to thematic mapping is also established.     

Identification of hazelnut fields using spectral and Gabor textural features

Land cover identification and monitoring agricultural resources using remote sensing imagery are of great significance for agricultural management and subsidies. Particularly, permanent crops are important in terms of economy (mainly rural development) and environmental protection. Permanent crops (including nut orchards) are extracted with very high resolution remote sensing imagery using visual interpretation or automated systems based on mainly textural features which reflect the regular plantation pattern of their orchards, since the spectral values of the nut orchards are usually close to the spectral values of other woody vegetation due to various reasons such as spectral mixing, slope, and shade. However, when the nut orchards are planted irregularly and densely at fields with high slope, textural delineation of these orchards from other woody vegetation becomes less relevant, posing a challenge for accurate automatic detection of these orchards. This study aims to overcome this challenge using a classification system based on multi-scale textural features together with spectral values. For this purpose, Black Sea region of Turkey, the region with the biggest hazelnut production in the world and the region which suffers most from this issue, is selected and two Quickbird archive images (June 2005 and September 2008) of the region are acquired. To differentiate hazel orchards from other woodlands, in addition to the pansharpened multispectral (4-band) bands of 2005 and 2008 imagery, multi-scale Gabor features are calculated from the panchromatic band of 2008 imagery at four scales and six orientations. One supervised classification method (maximum likelihood classifier, MLC) and one unsupervised method (self-organizing map, SOM) are used for classification based on spectral values, Gabor features and their combination. Both MLC and SOM achieve the highest performance (overall classification accuracies of 95% and 92%, and Kappa values of 0.93 and 0.88, respectively) when multi temporal spectral values and Gabor features are merged. High F_β values (a combined measure of producer and user accuracy) for detection of hazel orchards (0.97 for MLC and 0.94 for SOM) indicate the high quality of the classification results. When the classification is based on multi spectral values of 2008 imagery and Gabor features, similar F_β values (0.95 for MLC and 0.93 for SOM) are obtained, favoring the use of one imagery for cost/benefit efficiency. One main outcome is that despite its unsupervised nature, SOM achieves a classification performance very close to the performance of MLC, for detection of hazel orchards.     

基于NDVI时序数据的水稻种植面积遥感监测分析——以江苏省为例

MODIS植被指数时间序列产品能够连续反映植被的覆盖情况,是农作物遥感测量的重要数据源.本文选取江苏省为研究区,利用2008年23个时相的MODIS NDVI数据,采用S-G滤波法进行时间序列的重构,提高NDVI时间序列信息的真实性.另结合农作物物候历、种植结构、地面调查样本等辅助资料,将水稻植被指数时间序列曲线参量化...