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.     

广西德保铜锡矿床控矿因素及外围找矿预测

文章通过对德保铜锡矿床的控矿因素的综合分析,并结合地质物化探成果,提出2个成矿预测区,即钦甲矿区边部预测区和同德预测区,为继续开展找矿勘查工作、扩大危机矿山的资源量提供靶区.     

八家子银多金融矿矿田成矿规律与成矿预测

八家子矿田由NW、NE向两组断裂带与孤形推覆构造交织，控制构成银铅锌、银铁矿床成矿系列，基成矿组份具有组合分带特征。预测东山－冰沟银、铅、锌、炉沟钼和冰沟里银多金属找矿区，经验证，获得找矿成效。     

基于多传感器遥感数据和改进的单类随机森林的滑坡提取

提出一种利用面向对象多特征和改进的单类随机森林分类器（improved one-class random forest classifier,iOCRF）从多传感器遥感数据提取滑坡的新方法。从两时相高分辨率多光谱图像和中分辨率数字高程模型（DEM）数据中提取的图像对象多特征,来定量表达滑坡引起地表变化和滑坡区地形特征,利用iOCRF对所有特征图像进行分类以提取滑坡。据研究区两时相高分辨多光谱图像和30 m分辨率的ASTER全球DEM数据对提出的方法进行评价。结果表明,所提出方法的提取精度优于只利用部分特征提取和利用传统随机森林的提取精度;利用iOCRF计算不同特征的重要性,并分析不同特征对滑坡提取的贡献。     

基于支持向量回归和贝叶斯优化的海岸线预测

模拟预测海岸线的动态变化对于沿海地带的开发和科学管理有较重要意义。在获取围头湾研究区Landsat-8卫星时序影像和验潮站数据的基础上,利用支持向量回归模型,结合贝叶斯优化获取最佳超参数,进而预测得到未来年份的海岸线数据,并采用ROC匹配、平均偏移量和均方根误差3种方法验证预测得到的海岸数据。结果表明：基于贝叶斯优化的支持向量回归模型,可以较好地预测围头湾区域未来海岸线,对应的完整度、正确度和质量分别为87.06%、86.10%和80.05%,平均偏移量和均方根误差分别为0.64像素和0.96像素;对于自然岸线预测结果较好,而人工岸线预测部分结果较差。     

低频甚低频传播突然扰动

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