Evaluation of effectiveness of three fuzzy systems and three texture extraction methods for building damage detection from post-event LiDAR data

Building damage maps after disasters can help us to better manage the rescue operations. Researchers have used Light Detection and Ranging (LiDAR) data for extracting the building damage maps. For producing building damage maps from LiDAR data in a rapid manner, it is necessary to understand the effectiveness of features and classifiers. However, there is no comprehensive study on the performance of features and classifiers in identifying damaged areas. In this study, the effectiveness of three texture extraction methods and three fuzzy systems for producing the building damage maps was investigated. In the proposed method, at first, a pre-processing stage was utilized to apply essential processes on post-event LiDAR data. Second, textural features were extracted from the pre-processed LiDAR data. Third, fuzzy inference systems were generated to make a relation between the extracted textural features of buildings and their damage extents. The proposed method was tested across three areas over the 2010 Haiti earthquake. Three building damage maps with overall accuracies of 75.0%, 78.1% and 61.4% were achieved. Based on outcomes, the fuzzy inference systems were stronger than random forest, bagging, boosting and support vector machine classifiers for detecting damaged buildings.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

微动技术在堤坝渗漏探测中的应用

堤坝是重要的水利工程,其安全性至关重要。坝体渗漏是常见的安全隐患之一,对坝体渗漏位置、程度以及坝体结构变化的探测一直是重要的物探课题。微动勘探作为一种新兴的物探手段,应用范围正逐步拓展,通过某水库大坝渗漏检测实例,展示频率-波数法微动线性点阵剖面测量进行大坝渗漏检测的效果,该技术通过提取频散曲线及视S波速度成像,刻画地层速度结构并圈定异常,可实现对大坝渗漏快速精确的无损探测,具有一定的方法优势和广泛的应用前景。     

人工智能在冰雹识别及临近预报中的初步应用

基于广东10部S波段多普勒天气雷达的三维拼图资料,利用机器学习技术开发了一种冰雹识别和临近预报的人工智能算法。算法设计时以雷达回波反射率的垂直和水平扫描数据为基础训练集,将冰雹云的雷达反射率扫描数据作为正样本,将其他雷达反射率扫描数据作为负样本,通过贝叶斯分类法对正、负样本数据集进行机器学习,训练人工智能识别冰雹云内在规律的能力。训练时以广东省2008-2013和2015-2016年的数据作为训练集,使用了2014年广东省12次冰雹过程的数据做检验。对比检验的结果表明,人工智能法比传统的概念模型法击中率高9个百分点。研究结果表明了人工智能对冰雹这类非线性强天气过程具有较强的识别能力。      

基于镜像图的LRC和CRC偏差结合的人脸识别

为了提高人脸识别率及更好地显示人脸特征,本文提出了一种基于镜像图的LRC和CRC偏差结合的人脸识别方法.该方法首先生成一种镜像人脸,再通过融合原始人脸和镜像人脸形成新的混合训练样本,最后利用LRC和CRC偏差结合进行人脸识别.新方法增加了训练样本的数目,克服了由于光照和姿态等外部因素带来的影响.实验结果表明,镜像图与LRC和CRC偏差结合的人脸识别方法提高了人脸识别的准确性.     

安徽省地电监测能力综合评价

以影响地电观测质量的主要因素及映震能力作为地电台站监测能力的主要评价指标,对安徽省5个地电台站的监测能力进行了综合评价,得到了安徽省地电前兆的基本情况,为安徽省未来的地电台站建设奠定了基础.     

微震研究与应用进展

随着地震观测台站密度的不断增加以及地震检测技术的快速发展,微震研究受到了地震学界的广泛关注.与发震周期较长的大地震相比,微震复发周期短、发生频次高,可以获得更高分辨率的地壳内部介质物性和应力状态等变化信息.微震在许多领域具有广泛的应用,如研究断层几何形态、前震与地震成核的关系、余震时空演化特征及余震触发机理、远程动态触...     

基于显著性图像与纹理特征的遥感影像云检测

遥感影像云检测是遥感影像处理中非常关键的环节,准确识别影像含云区域能够提升影像的利用价值.根据遥感影像的成像特点,将阈值法和纹理特征结合实现云和下垫面的分割.首先将影像从RGB(red-green-blue)空间转化为HSI(hue-saturation-intensity)空间,进而构建影像的显著性图像,利用Otsu...     

流体替代技术及应用分析

地震技术是研究隐蔽油气藏的关键技术。岩石物理是地震信号与储层性质之间的桥梁,它可以帮助我们更好的理解岩石和流体性质的地震响应。流体替代分析是岩石物理分析的重要部分,它是流体识别和储层定量研究的工具之一,在AVO分析中起了重要的作用。流体替代的理论基础是Gassmann方程。在岩石物理技术指导下,对某工区探井进行了流体替代分析,并根据替代后的结果分析了波阻抗随深度、岩性和含油气性变化的规律,同时根据替代结果进行了AVO正演研究,比较分析了储层中流体分别为盐水、气和油时的响应特征,并据此根据实际的部分叠加道集进行了烃类异常识别。     

基于IP地址聚类的网络异常流量分析

异常流量在目的地址与出现时间上的分布均与正常流量有很大区别。文中对校园网的出口流量进行分析实验,将其NetFlow记录按校外IP地址的前16位聚类,得到的部分网段在出入流量中的出现频度有明显特点。分析2种典型网段,研究由此发现校园网内的异常流量源的方法,并对2种异常流量源的区别进行了分析。此方法与常用的异常检测方法相比,所需处理的数据量大为减少,大大提高了检测效率。