全省国土空间基础数据共享应用现场会在缙云召开

为深入贯彻落实省委、省政府“打破信息孤岛,实现信息共享,加快政府数字化转型、推进‘最多跑一次’改革向纵深发展”的要求,加快全省国土空间基础数据共享应用,提升国土资源信息化水平,4月16日-17日,     

应用GAMIT进行高精度GPS基线数据处理

GAMIT是美国麻省理工学院(MIT)与斯克里普斯海洋研究所(S1())研制的一个大型而复杂的GPS数据处理软件,是目前国际上优秀的高精度GPS数据处理通用软件之一.本文主要介绍了GAMIT的数据处理过程和使用方法,并结合实例对GAMIT数据处理结果精度进行了分析.     

扎实做好第二次土地调查各项工作

国务院决定,今年7月1日将启动第二次全国土地调查,计划用3年时间,全面查清全国范围的土地利用状况,掌握真实的土地基础数据。[第一段]     

2019—2020年1—3月降水和气温的多源数据融合产品检验评估及对比分析

对2020年1-3月CLDAS多源降水融合实况、多源气温融合实况采用平均绝对误差分析、均方根误差分析及相关系数分析等方法进行检验评估,并与2019年同时段多源降水融合实况、多源气温融合实况进行对比分析.结果表明:2月降水多源融合实况较观测实况偏多;3月多源气温融合实况最接近观测实况;西部地区的日最低气温多源融合实况较为接近观测实况.     

地球空间信息相关论文摘要

基于球面投影的单站地面激光扫描点云构网方法;应用聚类分析方法进行实测重力数据的选点优化;产生最佳Tuned模板的蜜蜂交配算法;移动道路测量系统及其在科技奥运中的应用;三维工程勘察信息系统建设及其应用     

微软和ESRI联合推出数据融合解决方案

近日,在加利福尼亚州圣地亚哥举行的ESRI全球用户大会上,微软公司和ESRI公司联合推出了数据融合解决方案。这是一个支持公共安全与国土安全解决方案的体系结构。该解决方案由两个业界领导者开发,用于帮助公共安全和国土安全领域的专业人员更加有效地防御当前不断增加的有形和无形的安全威胁。此外,该解决方案还可以增强政府机构预测、评估和应对自然灾害的能力。     

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.