Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping

Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.     

一种解码接收机原始二进制数据的方法

本文对华迅G PS/BD双模接收机输出的原始二进制数据的格式进行了深入讨论,利用M atlab对其输出的二进制数据的解码并提取出观测值、星历等,该方法可以简捷快速的解码出所需要的观测信息。     

数据字典在地质矿产术语分类代码修编平台中的应用

基于B/S模式的《地质矿产术语分类代码》（GB 9649-1988）修编平台是提高该标准修编工作效率的一种必然手段,而数据字典在该平台中的穿插使用保证了修编工作的合理与安全.通过论述数据字典技术在实现修编平台的修编权限控制、修编版本管理功能中的具体应用,体现了数据字典技术在数据处理方面的独特优势.数据字典技术在该平台中的应用,不仅对修编人员的修编权限进行了灵活的约束,而且修编过程中产生的不同版本也得到了有效的管理.     

物探软件MAGS2.0在峨腊厂高精度磁测中的应用

划分出3个磁异常场区,Ⅲ区规模较大.出露中元古界昆阳群大龙口组,是勘查区内铁矿床的赋矿层位,受断裂构造和岩体控制.     

大数据环境下地理信息产业发展的几点思考

大数据背景下地理信息产业的发展有了新的驱动力,通过思考这种驱动力对产业发展可能产生的影响,分析指出了大数据在地理信息产业中的中心地位和重要价值,综述了大数据给地理信息产业的涵盖范围、关键技术和服务模式所带来的重大变革,探讨了大数据技术对于GIS传统技术优化升级的几个着力点,并剖析了大数据技术在地理信息产业的相关应用中所面临的制约因素。     

浅析昆明国土资源“大数据”建设

本文探讨昆明市国土资源数据管理部门应如何使用大数据技术来管理、利用数据,发掘数据之中的价值,为社会服务、政府决策提供支持。同时,探索如何部署大数据技术、建立技术机制和人才保障制度。     

江苏省市、县级矿产资源规划数据库建设及应用研究

矿产资源规划数据库作为矿产资源规划成果内容的一部分,是矿产资源规划管理工作的基础数据。本文针对江苏省市、县级矿产资源规划数据库建设实际,对数据库建设的方法进行探讨,提出了第二轮矿产资源规划数据库建设中的问题和解决对策,并开展矿产资源规划数据归集与应用研究,可为第三轮矿产资源规划及其他数据库建设提供相关参考。     

大数据视角下的地矿工作发展与变革研究

本文提出了地矿大数据应用需要考虑地球系统和社会系统两个方面的内部规律,分析了地矿大数据的三类组成,给出了地矿大数据的四层应用架构;重点就大数据下地矿工作发展和变革提出了四项建议,包括重视数据,转变地矿工作决策模式;收集数据,夯实地矿工作决策基础;分析数据,提高地矿工作科学决策水平;开放数据,提高地矿工作服务能力和水平。     

数字地图生产流程中质量的数据控制

本文将着重讨论在ISO9001质量控制体系下、数字地图生产流程中影响地图质量的各类数据文件的设定与控制,从而进一步探讨在电子出版系统及CTP系统中,数字地图生产规范化、标准化、程序化作业的质量控制内容及方法。     

Point pattern analysis of north polar crescentic dunes, Mars: A geography of dune self-organization

The geographic signature of dune distribution and self-organization as measured by the R-statistic offers a viewpoint on the geography of crescentic eolian systems and proposes an index from which to determine the degree of self-organization across a variety of spatial scales. Fields of simple dunes (dome, barchan, barchan-seif) are comparatively less regular in distribution than are those fields, or part thereof, that consist of compound (barchanoid) morphologies whose patterns are more highly regular.