基于FY-4A卫星数据的短时强降水监测预警指标

选取2018—2021年汛期短时强降水天气过程,利用相关性分析、箱线图法和极值统计法,尝试研究FY-4A卫星产品在短时强降水天气过程中的监测预警指标。研究表明：(1)FY-4A卫星多通道数据可以作为短时强降水监测预警的定量化指标予以应用。(2)筛选出相关性较好的13项产品统计出短时强降水的监测预警指标,其中赋值类指标4项,数值判别类指标9项(含辅助指标3项);初步设定13项指标中有9项达标时,短时强降水会发生。(3)在评估基础上完善了指标,监测预警效果有所提高,TS评分提高5.4%,空报率降低2.7%,漏报率降低1.9%。     

测绘工作对抗灾救灾和灾后重建的意义

航天遥感获取遥感影像为灾后灾害评估提供资料和数据分析 重大突发性火害爆发的时候,一般伴有通讯终止、交通瘫痪、受灾地区基础设施遭受重大破坏等紧急情况,往往让救灾部门无法及时获取准确的地形资料,从而不能对灾害进行一个整体的、精确的评估.     

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一种基于谷地填充的DEM综合方法;GOCE卫星轨道摄动的数值模拟与分析;全球地图第一版中国土地覆盖产品的生成;GPS信号载噪比估计算法的研究;基于支持向量机的景象匹配区选择方法     

科技引领导航产业持续发展

卫星导航应用产业在国民经济建设中占有重要的地位,是国民经济信息化建设的重要组成部分和推进力量。全球导航卫星系统（GNSS）能够迅速、     

“第21届ISPRS大会论文综述”专题前言

2008年7月3日．11日,第21届国际摄影测量与遥感学会（ISPRS）大会在北京成功召开,本届大会以“铺就影像信息的丝绸之路”为主题,针对当前摄影测量、遥感和地理空间信息领域的国际前沿与热点问题,设置了23个专题单元、19个特别单元、63个工作组单元、59个张贴报告单元、3个用户论坛单元、1个青年论坛等150个学术技术单元进行学术交流。     

Extending self-organizing maps for supervised classification of remotely sensed data

An extended self-organizing map for supervised classification is proposed in this paper. Unlike other traditional SOMs, the model has an input layer, a Kohonen layer, and an output layer. The number of neurons in the input layer depends on the dimensionality of input patterns. The number of neurons in the output layer equals the number of the desired classes. The number of neurons in the Kohonen layer may be a few to several thousands, which depends on the complexity of classification problems and the classification precision. Each training sample is expressed by a pair of vectors ： an input vector and a class codebook vector. When a training sample is input into the model, Kohonen＇s competitive learning rule is applied to selecting the winning neuron from the Kohouen layer and the weight coefficients connecting all the neurons in the input layer with both the winning neuron and its neighbors in the Kohonen layer are modified to be closer to the input vector, and those connecting all the neurons around the winning neuron within a certain diameter in the Kohonen layer with all the neurons in the output layer are adjusted to be closer to the class codebook vector. If the number of training sam- ples is sufficiently large and the learning epochs iterate enough times, the model will be able to serve as a supervised classifier. The model has been tentatively applied to the supervised classification of multispectral remotely sensed data. The author compared the performances of the extended SOM and BPN in remotely sensed data classification. The investigation manifests that the extended SOM is feasible for supervised classification.