波段选择协同表达高光谱异常探测算法

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。     

基于时间序列叶面积指数稀疏表示的作物种植区域提取

以华北平原黄河以北地区为研究区域,以时间序列叶面积指数LAI(Leaf Area Index)傅里叶变换的谐波特征作为不同作物识别的数据源,利用稀疏表示的分类方法识别2007年—2016年冬小麦、春玉米、夏玉米等主要农作物种植区域。首先利用上包络线Savitzky-Golay滤波分别对2007年—2016年的时间序列MODIS LAI曲线进行重构,进而对重构的年时间序列LAI进行傅里叶变换,以0—5级谐波振幅、1—5级谐波相位作为作物识别的依据,基于各类地物的训练样本,通过在线字典学习算法构建稀疏表示方法的判别字典,对每个待测样本利用正交匹配追踪算法求解稀疏系数,从而计算对应于各类地物的重构误差,根据最小重构误差判定待测样本的作物类型,并对作物识别结果的位置精度进行验证。结果表明,2007年—2016年作物识别的总体精度为77.97%,Kappa系数为0.74,表明本文提出的方法可以用于研究区域主要作物种植区域的提取。     

三维城市模型的研究与实践(英文)

The way we interact with spatial data has been changed from 2D map to 3D Virtual Geographic Environment (VGE). Three-dimensional representations of geographic information on a computer are known as VGE, and in particular 3D city models provide an efficient way to integrate massive, heterogenous geospatial information and georeferenced information in urban areas. 3D city modeling (3DCM) is an active research and practice topic in distinct application areas. This paper introduces different modeling paradigms employed in 3D GIS, virtual environment, and AEC/FM. Up-to-date 3DCM technologies are evolving into a data integration and collaborative approach to represent the full spatial coverage of a city, to model both aboveground and underground, outdoor and indoor environments including man-made objects and natural features with 3D geometry, appearance, topology and semantics. Supported by the National Natural Science Foundation of China ( No. 40871212, No. 40671158), the Leading Academic Discipline Project of Shanghai Educational Committee( No.J50104).     

Verification of significant wave representation method

The significant wave representation method is the simplest method for computing the transformation of significant wave height across-shore. However, many engineers are reluctant to use this method because many researchers have pointed out that the method possibly contains a large estimation error. Nevertheless, Rattanapitikon et al. [Rattanapitikon, W., Karunchintadit, R., Shibayama, T., 2003. Irregular wave height transformation using representative wave approach. Coastal Engineering Journal, JSCE 45(3), 489–510.] showed that the wave representation method could be used to compute the transformation of root mean square wave heights. It may also be possible to use it for computing the significant wave height transformation. Therefore, this study was carried out to examine the possibility of simulating significant wave height transformation across-shore by using the significant wave representation method. Laboratory data from small- and large-scale wave flumes were used to calibrate and examine the models. Six regular wave models were applied directly to irregular waves by using the significant wave height and spectral peak period. The examination showed that three regular wave models (with new coefficients) could be used to compute the significant wave height transformation with very good accuracy. On the strength of both accuracy and simplicity of the three models, a suitable model is recommended for computing the significant wave height transformation. The suitable model was also modified for better predictions. The modified model (with different coefficients) can be used to compute either regular wave height or significant wave height transformation across-shore.     

一种改进的基于转角函数法的面状居民地Morphing变换方法

提出了一种顾及位置偏差与凹部同异性的面状居民地Morphing变换转向角函数方法.该方法通过构造大比例尺居民地的凸包作为中间图形,辅助与小比例尺居民地进行特征顶点匹配,避免因位置偏差造成的特征点误匹配;针对大比例尺居民地的凹部,依据凹部的模式类型,判定凹部边的同异性;最后构建插值模型,得到中间任意尺度下的面状居民地要素...     

云南1月降水异常的气候成因分析

在云南旱季降水稀少的气候背景下发生的异常旱涝气候事件是短期气候预测面临的难点之一。开展旱季降水异常的气候成因分析,对于提高气候预测准确率具有重要意义。1月是云南隆冬季节的主要时段,降水变化具有明显的偏态分布特征,年际波动非常显著。云南1月降水偏多年与偏少年大气环流有明显差异,500 hPa高度场上有四个区域位势高度与云南1月降水显著相关,中高纬的三个区域分别与斯堪的纳维亚(SCA)型遥相关的三个活动中心对应,低纬阿拉伯海的高度异常与孟加拉湾印缅槽活动有密切联系。当SCA为正(负)位相且阿拉伯海高度负(正)异常时云南1月降水偏少(多)。海洋上两个显著海温影响区域分别位于东南太平洋和北大西洋中西部,海温异常主要通过影响东亚和南亚地区的环流进而对云南1月降水产生影响。利用高度场和海温场的关键区域的数据分别合成高度场和海温场的组合序列,组合序列与云南1月降水有更高的相关性和更好的距平符号对应关系。在高度场和海温场组合序列的散点图上,近80%的年份位于第一和第三象限,这两个区域高度场和海温场的配置对云南1月降水作用一致,落区位置与降水距平符号对应关系很好;其余约20%的年份位于第二和第四象限,高度场和海温场的作用相反,落区位置与降水距平符号对应关系较差。     

A formal framework for the representation of stack-based terrains

This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework.     

主题学习和稀疏表示的MODIS图像超分辨率重建

针对MODIS图像分辨率受传感器限制和噪声干扰,且分辨率局限在一定水平等问题,提出一种采用主题学习和稀疏表示的MODIS图像超分辨率重建方法,该方法通过双边滤波将MODIS图像的平滑及纹理部分分离,并将纹理部分看成是由若干"文档"组成的训练样本;运用概率潜在语义分析提取"文档"的潜在语义特征,从而确定"文档"所属的"主题"。在此基础上,针对每个主题所对应的图像块,采用改进的K-SVD方法训练若干适用于不同主题的高低分辨率字典对,从而可以运用这些字典对,通过稀疏编码实现测试图像相应主题块的超分辨率重建。实验结果表明,重建图像在视觉效果和PSNR等指标上均优于传统方法。     

基于机器学习的南京市旅游地个性及其文化景观表征

旅游地具有物质和精神双重属性。鉴于旅游地精神属性在量化表达和测量方面的困难,以往旅游地研究较少涉及。旅游地个性概念的提出为旅游地精神属性研究打开了思路。由于开展时间不长,当前旅游地个性研究大多基于营销学中的品牌个性研究思路展开,缺乏对地方适用性的考量,且尚未深入到对个性表征形式及表征机制的探讨。基于此,从地理学视角出发,选取城市旅游地作为研究对象,着眼地方特性,系统构建城市旅游地个性量表,在此基础上,开展针对案例地城市南京的个性测量和分析,并以城市文化景观作为表征媒介,深入探究城市旅游地个性的景观表征形式及表征机制。研究发现：城市旅游地个性主要包含态度、气质、性格、能力四大特征;城市文化景观是城市旅游地个性的重要表征媒介,包含现代空间景观、传统空间景观、生态景观、生活景观、社会景观五大维度;以上景观维度及其所辖景观元素在表征城市旅游地个性方面发挥不同作用。研究过程及结论为包括旅游地在内的地方精神属性研究提供了一种概念框架和方法体系,是对精神层面人地关系认识上的丰富。机器学习这一智能化探索型数据分析手段的运用为处理地理学中普遍存在的高维非线性噪声数据提供了有力的方法支撑。