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合成孔径雷达(SAR)凭借其全天候观测能力以及SAR图像中丰富的纹理信息,在震后建筑物倒塌评估中发挥了重要作用。针对SAR图像中倒塌建筑物纹理特征多样但利用率较低,且特征信息冗余的问题,提出一种基于主成分分析的SAR图像多纹理特征分类方法。该方法基于灰度直方图、灰度共生矩阵、局部二值模式、Gabor滤波器提取了26种纹理特征信息,构建主成分变量进行多维特征优选与降维融合,通过随机森林分类算法提取建筑物的倒塌信息。以2016年日本熊本地震为例验证了该方法的有效性,结果显示其提取精度高达79.85%,倒塌建筑物的识别效率有所提高,分类结果优于单种纹理特征提取方法及多种纹理特征组合提取法,可用于震后建筑物震害信息的快速提取。 相似文献
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利用震后1景极化SAR影像提取倒塌建筑物是一种快速有效的灾害调查手段。倒塌建筑和倾斜建筑物在PolSAR影像中的散射特征相似,易造成建筑物倒塌率的过度评估。由于倒塌建筑和倾斜建筑的纹理特征有较大差异,将利用这种纹理差异来解决倒塌建筑和倾斜建筑的混分问题。通过实验发现均值、同质性、熵及相关性4种基于灰度共生矩阵(Gray-Level Co-occurrence Matrix,GLCM)的纹理特征能够有效区分倾斜建筑和倒塌建筑,故利用这4种纹理特征提取倒塌建筑中混杂的倾斜建筑,从而降低倒塌建筑的虚警率。以玉树地震为例,提取城区的建筑物震害信息,实验证明该方法能够大幅提高建筑物震害评估精度。 相似文献
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目的 格式塔心理学的理论基础为通过对事物的部分感知,实现对事物整体的认识。本文将该思想应用到建筑物提取中,提出一种兼顾目标细节及整体几何特征的高分辨率遥感影像建筑物提取方法。方法 首先,利用SIFT算法提取特征点作为候选边缘点;然后定义格式塔序列连续性原则判别边缘点,从而得到边缘点点集;并由边缘点点集拟合边缘,实现遥感影像建筑物提取。结果 利用提出算法,对WorldView-2遥感影像进行建筑物提取实验。通过与基于多尺度分割和区域合并的建筑物提取算法对比可以看出,提出算法能够更加准确、完整地提取出建筑物。采用分支因子、遗漏因子、检测率和完整性4个定量化指标对实验结果的定量评价,本文算法的检测率和完整性均大于对比算法,且本文算法的检测率均在95%以上,验证了提出基于格式塔理论的高分辨率遥感影像建筑物提取算法的有效性和准确性。结论 基于格式塔的高分辨率遥感影像建筑物提取算法能够准确刻画建筑物细节特征,同时兼顾建筑物整体几何轮廓,准确提取高分辨率遥感影像中的建筑物。本文算法针对高分辨率遥感影像,适用于提取边缘具有直线特征的建筑物。使用本文算法进行遥感影像建筑物提取时,提取精度会随分辨率降低而降低,建议实验影像分辨率在5 m以上。 相似文献
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破坏性地震发生后, 相较按照抗震设防标准建设的城市公共设施和居民住宅, 广大无抗震设防的村镇居民自建房屋, 更易发生倒塌甚至完全损毁. 以往地震灾情预评估、地震灾害风险调查、地震重点危险区调研, 依靠专家现场踏勘, 确定不同结构类型建筑物数量及所占比例. 本研究借助深度学习和倾斜摄影技术, 进行砖(混)木结构房屋识别, 郯庐断裂带山东境内砖(混)木房屋影像制作数据集, 训练得到Faster R-CNN模型, 该区域内砖(混)木房屋识别平均精度为91.868%. 结果表明, 本文方法能够对砖(混)木房屋进行有效检测, 可应用于地震行业开展震前、震后各类现场工作, 提高政府部门应急管理能力. 相似文献
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针对震后高分辨率遥感图像的建筑物损毁区域,提出一种基于多特征结合的损毁建筑物检测方法。首先使用形态学属性剖面(MAP)与局部二值模式(LBP)算子提取图像中的几何特征与纹理特征;然后使用随机森林(RF)分类器提取损毁的建筑物,形成初步结果;最后针对分割的对象,根据对象损毁像元所占的比例获取最终的损毁建筑物区域。采用空间分辨率为0.1 m的玉树震后航空遥感图像进行实验。结果表明,该方法的总体精度比基于形态学剖面(MP)的方法提高了12%,能够有效检测高分辨率震后遥感图像中的损毁建筑物区域。 相似文献
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针对传统建筑物提取方法中图像高层语义和低层视觉特征之间存在的语义鸿沟问题,提出一种基于卷积神经网络模型场景解译框架下的高铁沿线建筑物隐患区域自动识别方法。该方法首先将遥感影像重叠地划分成一系列的影像块,生成卷积神经网络模型输入的基本单元;然后,根据训练样本集,学习获得模型参数,并预测每个待解译影像块内各地物类别的概率分布;最后,原始影像中每个像素的地物类别由所有覆盖该像素影像块的场景类别所共同确定,继而将获得的多分类图转化为二值分类图,实现建筑物区域的自动识别。2 675×6 465的大场景高铁沿线遥感影像下开展的实验结果表明,该方法建筑物提取精度明显优于传统分类方法,提取结果的紧凑性和平滑性得到显著提升,与地表真实值吻合度较高。 相似文献
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Xue Wang 《International journal of remote sensing》2013,34(8):2163-2183
Extraction of urban building damage caused by earthquake disasters, from very-high-resolution (VHR) satellite imagery and related geospatial data, has been widely studied in the past decade. In this study, a multi-stage collapsed building detection method, using bi-temporal (pre- and post-earthquake) VHR images and post-earthquake airborne light detection and ranging (lidar) data, is proposed. Ground objects that are intact and significantly different from collapsed buildings, such as intact buildings, pavements, shadows, and vegetation, were first extracted using the post-event VHR image and lidar data and masked out. Collapsed buildings were then extracted by classifying the combined bi-temporal VHR images and texture images of the remaining area using a one-class classifier, the One-Class Support Vector Machine (OCSVM). A post-processing procedure was adopted to refine the obtained result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. In the two comparative methods, data for the whole study area were directly used. In the first method, collapsed buildings were extracted directly using the OCSVM, while in the second method, buildings and pavements were removed from the extraction result of the first method. The results showed that the proposed method significantly outperformed the existing methods, with increases of 21% and 40%, respectively, in the kappa coefficient. The proposed method provides a fast and reliable method to detect collapsed urban buildings caused by earthquake disasters, and could also be applied to other study areas using similar data combinations. 相似文献
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Integrated earthquake simulation (IES) is a seamless simulation of the three earthquake processes, namely, the earthquake hazard process, the earthquake disaster process and the anti-disaster action process. High performance computing (HPC) is essential if IES, or particularly, the simulation of the earthquake disaster process is applied to an urban area in which 104∼6 structures are located. IES is enhanced with parallel computation, and its performance is examined, so that virtual earthquake disaster simulation will be made for a model of an actual city by inputting observed strong ground motion. It is shown that parallel IES has fairly good scalability even when advanced non-linear seismic structure analysis is used. 相似文献
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针对传统防震减灾系统的不足,引入Agent技术构造了一个基于多Agent的防震减灾智能决策支持系统。首先构建了系统的基本结构,然后根据系统的不同功能需要对所使用的各个Agent进行设计,最后分析了各Agent之间的协作关系,构建系统的协作关系模型并完成整个系统的设计。系统中的Agent按照Goal、Plan、Belief和Action四个部分进行架构,Agent之间的复杂协作关系采用对相关Agent进行分组控制的方法来实现。 相似文献
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灾情数据自动获取的地震灾情信息系统 总被引:2,自引:0,他引:2
为了及时掌握地震规模、空间分布、次生灾害,以及灾情发展趋势等灾情信息,将地震灾情数据采集装置与地震灾情数据管理系统相结合,建立了一种基于灾情数据自动获取的地震灾情信息系统.地震灾情数据采集装置配有多个信息采集单元,能够在感震器的触发下自动采集与上传地震灾情数据.而灾情数据管理系统基于三层体系结构的开发模型构建,提高了数据管理系统的响应速度和事件处理能力.系统集成实验表明,地震灾情信息系统能够独立完成地震灾情数据的自动采集、集中管理与应用共享. 相似文献
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There are thousands of earthquakes every year, but most of them are small and are not disasters. This study explored whether non‐destructive earthquake experience affects public risk perception and motivates preparedness. The study respondents were from the Jiaodong Peninsula, China, where more than 20 small earthquakes have occurred during the past decade. The results show that non‐destructive earthquake experience positively affected public perception of the probability of an earthquake but not the perception of consequences. The relationship between non‐destructive earthquake experience and preparedness intention was not statistically significant. In addition, this study revealed that the perception of consequences was a positive predictor of earthquake preparedness intention, that is people with higher levels of perceived consequences of an earthquake were more likely to be motivated to prepare. However, non‐destructive earthquake experience moderated this relationship, that is weakened the strength of this relationship between perceived consequences and skill preparedness intention. These findings permit the identification of potential pathways to increase levels of disaster preparedness and potential barriers to enhancing disaster resilience in regions experiencing small hazards. 相似文献
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In this study, the post-earthquake aerial photographs were digitally processed and analysed to detect collapsed buildings caused by the Izmit, Turkey earthquake of 17 August 1999. The selected area of study encloses part of the city of Golcuk, which is one of the urban areas most strongly hit by the earthquake. The analysis relies on the idea that if a building is collapsed, then it will not have corresponding shadows. The boundaries of the buildings were available and stored in a Geographical Information System (GIS) as vector polygons. The vector building polygons were used to match the shadow casting edges of the buildings with their corresponding shadows and to perform analyses in a building-specific manner. The shadow edges of the buildings were detected through a Prewitt edge detection algorithm. For each building, the agreement was then measured between the shadow producing edges of the building polygons and the thresholded edge image based on the percentage of shadow edge pixels. If the computed percentage value was below a preset threshold then the building being assessed was declared as collapsed. Of the 80 collapsed buildings, 74 were detected correctly, providing 92.50% producer's accuracy. The overall accuracy was computed as 96.15%. The results show that the detection of the collapsed buildings through digital analysis of post-earthquake aerial photographs based on shadow information is quite encouraging. It is also demonstrated that determining the optimum threshold value for separating the collapsed from uncollapsed buildings is important. 相似文献