Alternative Fuzzy Cluster segmentation of remote sensing images based on Adaptive Genetic Algorithm

Remote sensing image segmentation is the basis of image understanding and analysis. However, the precision and the speed of segmentation can not meet the need of image analysis, due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm (AGA) and Alternative Fuzzy C-Means (AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased, and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means (FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.     

地理信息技术支持的“多规合一”——以常州市新北区为例

开展"多规合一"是推进生态文明建设、新型城镇化建设,落实供给侧改革的必然要求。本文以地理信息技术为基础,以土地利用规划为底盘,统一城乡规划、环保规划、农业发展规划等的空间基础;以经济社会发展规划为目标协调各规划,划定永久基本农田保护红线、生态保护红线、城市开发边界,实施严格的空间管控。最后,以常州市新北区为试验区,分析各规划的差异并落实重点项目,引导国土空间有序开发,优化资源空间配置,形成国土空间开发规划"一张图"。     

冀西安妥岭斑岩型钼矿成矿地质条件浅析

安妥岭钼矿位于燕辽钼（铜）成矿带南端,与附近的野弧斑岩型钼矿等具有相同的地质背景和成矿条件。文章阐述了古结晶基底、中生代岩体以及NNE、NW和近EW向构造与成矿的关系,提出安妥岭钼矿是在斑岩型矿床基础上,叠加了主要沿NW向构造发育的岩浆热液成矿作用所形成的具有复杂成矿系列的非典型斑岩钼矿床。     

EEMD-多尺度排列熵的GPS高程时间序列降噪方法

针对GPS高程时间序列受各类噪声干扰的影响,导致难以提取有用信息的问题,提出一种基于整体经验模态分解(EEMD)结合多尺度排列熵(MPE)的阈值降噪方法。该方法以EEMD为核心算法,将原始信号分解成一系列本征模态函数(IMF),并采用MPE作为指标将其分类为噪声IMF、混合IMF和信息IMF;然后利用阈值函数处理混合IMF,实现二次降噪;再重构降噪后的数据与信息IMF,获得降噪结果。仿真信号和实例分析结果表明,该方法与相关系数法、MPE法相比,降噪评价指标RMSE、SNR和d_nSNR均为最优,说明该降噪方法效果最好,本文方法获得的降噪结果能够更好地反映出时间序列本身的非线性变化特性,可为GPS高程时间序列分析提供可靠依据。     

改进基于事件的时空数据模型

本文回顾了基于状态的时空数据模型和基于事件的时空数据模型。为了完整记录下时空的变化过程,有利于时空过程的查询,论文针对栅格数据提出了一种改进基于事件的时空数据模型。该模型完整地记录下地理现象的变化过程,并将地图数据的当前状态作为基图。由于大多应用中对现状图调用的最多,所以将现状图作为基图改进了系统的运行效率。最后,为了体现模型查询变化信息的优势,文章分析了一些时空查询实例。     

应对气候变化进程中纳入性别考虑的进展与启示

女性在应对气候变化中处于不利地位,也是应对气候变化中不可忽视的力量。国际社会已逐渐认识到在应对气候变化进程中纳入性别考虑的重要意义,性别议题在气候谈判中的主流化趋势明显,《联合国气候变化框架公约》执行层面纳入性别考虑取得了长足的进展,以全球环境基金、绿色气候基金为代表的《联合国气候变化框架公约》资金机制已将性别政策纳入机构政策体系,并积极在项目层面推动性别政策的实施。虽然国际气候变化领域的性别主流化工作取得了较大进展,但仍存在女性参与气候变化决策的程度不足、减缓领域纳入性别考虑的程度不足等问题。目前中国气候变化领域对性别的关注不足,建议加强气候变化领域的性别问题研究,为履约工作提供支撑;在应对气候变化相关的政策体系中纳入性别考虑,加强与性别领域的沟通协作;加强气候变化相关机构性别主流化能力建设,明确职责安排;加强气候变化领域国内项目、“一带一路”项目和“南南合作”项目的性别主流化;切实加强中国女性应对气候变化能力,提高女性对气候行动的参与度。     

面向流媒体传输的空间数据变化累积模型

将流媒体技术引入到以矢量数据为主的网络GIS传输中,其关键是建立逐级精细化的多层次表达模型,并在服务器端数据预组织上建立线性索引结构。本研究针对该目标建立一种矢量数据多重表达的新模型,即变化累积模型,认为尺度空间中从粗到细的数据表达过程是一些"变化"的累积,"变化"表现为两个连续尺度表达间的差别,通过"变化"元素的逐渐增加或减少来实现渐进传输。基于该模型对面状数据渐进式传输进行研究,将多边形分级剖分为一系列凸壳或外接矩形的基础上建立变化累积模型,通过剖分元素的组合完成渐进式流媒体传输。     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.     

Non-stationary covariance function modelling in 2D least-squares collocation

Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account for spatial dependence in the observed data. However, the assumption that the spatial dependence is constant throughout the region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of, e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from spatial statistics for non-stationary covariance structures, and demonstrate its advantage for dealing with non-stationarity in geodetic data. We then compared stationary and non- stationary covariance functions in 2D LSC to the empirical example of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary. The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve upon standard (stationary) LSC.     

一种自适应匹配子空间亚像元目标探测方法

为了克服基于线性混合模型的高光谱遥感影像亚像元目标探测方法的缺陷, 提出了一种基于全限制性线性分解的自适应匹配子空间探测方法。首先利用交叉相关光谱匹配技术求得各个像元所含端元类别信息, 然后根据端元类别信息和全限制性分解的结果构造自适应匹配子空间探测算子, 利用端元类别信息在探测中动态选择端元, 降低端元数目估计偏差对探测结果的影响, 提高探测器对目标与背景的可分性。实验证明, 该方法与其他基于线性混合模型的亚像元目标探测方法相比, 可以更好地克服端元数目估计偏差对探测结果的影响, 无论是端元个数低估还是