Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable dete...Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images.展开更多
利用天气观测资料、NECP/NCAR再分析资料对2001年1月—2015年8月发生在山东的32例气旋导致的大范围的暴雨过程进行了分析。将气旋分为黄河气旋型、黄淮气旋型与江淮气旋型,针对每类气旋重点分析其暴雨发生的动力机制、水汽特征,暴雨落区...利用天气观测资料、NECP/NCAR再分析资料对2001年1月—2015年8月发生在山东的32例气旋导致的大范围的暴雨过程进行了分析。将气旋分为黄河气旋型、黄淮气旋型与江淮气旋型,针对每类气旋重点分析其暴雨发生的动力机制、水汽特征,暴雨落区等,并建立了概念模型。结论如下:(1)黄河气旋型暴雨落区在气旋移动方向的左前方,暖锋附近,天气尺度强迫有利于暴雨产生,水汽来源于西南气流输送或气旋本身。(2)黄淮气旋型暴雨落区在气旋移动方向的左前方,属暖区降水,高低空急流垂直耦合诱发深对流,促使暴雨产生。(3)江淮气旋型暴雨落区在气旋中心北侧,属冷区降水,其环流形势经向度较大,诱使低层低涡切变线北移,为系统性暴雨的产生提供水汽条件和动力条件。(4)三类气旋暴雨过程中,对流层高层多为辐散场或高空急流入口区右侧,低层多有低涡配合;当有低空偏南风急流出现时,降水量大,反之,则小;暴雨中心均与850 h Pa水汽通量散度辐合区、高比湿区及高能舌区三者相叠置的位置相吻合。展开更多
流量分类是优化网络服务质量的基础与关键.机器学习算法利用数据流统计特征分类流量,对于识别加密私有协议流量具有重要意义.然而,特征偏置和类别不平衡是基于机器学习的流量分类研究所面临的两大挑战.特征偏置是指一些数据流统计特征...流量分类是优化网络服务质量的基础与关键.机器学习算法利用数据流统计特征分类流量,对于识别加密私有协议流量具有重要意义.然而,特征偏置和类别不平衡是基于机器学习的流量分类研究所面临的两大挑战.特征偏置是指一些数据流统计特征在提高部分应用识别准确率的同时也降低了另外一部分应用识别的准确率.类别不平衡是指机器学习流量分类器对样本数较少的应用识别的准确率较低.为解决上述问题,提出了基于集成聚类的流量分类架构(traffic classification framework based on ensemble clustering,简称TCFEC).TCFEC由多个基于不同特征子空间聚类的基分类器和一个最优决策部件构成,能够提高流量分类的准确率.具体而言,与传统的机器学习流量分类器相比,TCFEC的平均流准确率最高提升5%,字节准确率最高提升6%.展开更多
文摘Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images.
文摘利用天气观测资料、NECP/NCAR再分析资料对2001年1月—2015年8月发生在山东的32例气旋导致的大范围的暴雨过程进行了分析。将气旋分为黄河气旋型、黄淮气旋型与江淮气旋型,针对每类气旋重点分析其暴雨发生的动力机制、水汽特征,暴雨落区等,并建立了概念模型。结论如下:(1)黄河气旋型暴雨落区在气旋移动方向的左前方,暖锋附近,天气尺度强迫有利于暴雨产生,水汽来源于西南气流输送或气旋本身。(2)黄淮气旋型暴雨落区在气旋移动方向的左前方,属暖区降水,高低空急流垂直耦合诱发深对流,促使暴雨产生。(3)江淮气旋型暴雨落区在气旋中心北侧,属冷区降水,其环流形势经向度较大,诱使低层低涡切变线北移,为系统性暴雨的产生提供水汽条件和动力条件。(4)三类气旋暴雨过程中,对流层高层多为辐散场或高空急流入口区右侧,低层多有低涡配合;当有低空偏南风急流出现时,降水量大,反之,则小;暴雨中心均与850 h Pa水汽通量散度辐合区、高比湿区及高能舌区三者相叠置的位置相吻合。
文摘流量分类是优化网络服务质量的基础与关键.机器学习算法利用数据流统计特征分类流量,对于识别加密私有协议流量具有重要意义.然而,特征偏置和类别不平衡是基于机器学习的流量分类研究所面临的两大挑战.特征偏置是指一些数据流统计特征在提高部分应用识别准确率的同时也降低了另外一部分应用识别的准确率.类别不平衡是指机器学习流量分类器对样本数较少的应用识别的准确率较低.为解决上述问题,提出了基于集成聚类的流量分类架构(traffic classification framework based on ensemble clustering,简称TCFEC).TCFEC由多个基于不同特征子空间聚类的基分类器和一个最优决策部件构成,能够提高流量分类的准确率.具体而言,与传统的机器学习流量分类器相比,TCFEC的平均流准确率最高提升5%,字节准确率最高提升6%.
