A genetic classification of sinkholes illustrated from evaporite paleokarst exposures in Spain

This contribution analyses the processes involved in the generation of sinkholes from the study of paleokarst features exposed in four Spanish Tertiary basins. Bedrock strata are subhorizontal evaporites, and in three of the basins they include halite and glauberite in the subsurface. Our studies suggest that formation of dolines in these areas results from a wider range of subsidence processes than those included in the most recently published sinkhole classifications; a new genetic classification of sinkholes applicable to both carbonate and evaporite karst areas is thus proposed. With the exception of solution dolines, it defines the main sinkhole types by use of two terms that refer to the material affected by downward gravitational movements (cover, bedrock or caprock) and the main type of process involved (collapse, suffosion or sagging). Sinkholes that result from the combination of several subsidence processes and affect more than one type of material are described by combinations of the different terms with the dominant material or process followed by the secondary one (e.g. bedrock sagging and collapse sinkhole). The mechanism of collapse includes any brittle gravitational deformation of cover and bedrock material, such as upward stoping of cavities by roof failure, development of well-defined failure planes and rock brecciation. Suffosion is the downward migration of cover deposits through dissolutional conduits accompanied with ductile settling. Sagging is the ductile flexure of sediments caused by differential corrosional lowering of the rockhead or interstratal karstification of the soluble bedrock. The paleokarsts we analysed suggest that the sagging mechanism (not included in previous genetic classifications) plays an important role in the generation of sinkholes in evaporites. Moreover, collapse processes are more significant in extent and rate in areas underlain by evaporites than in carbonate karst, primarily due to the greater solubility of the evaporites and the lower mechanical strength and ductile rheology of gypsum and salt rocks.     

加入多尺度图像纹理的岩性分类

利用遥感图像进行岩性分类,是遥感地质应用的重要方面之一.本文运用地统计学中变差函数提取图像纹理,并与原始的光谱图像相结合用于遥感图像的岩性分类.文章分析了不同尺度的纹理信息对岩性分类的作用,并进一步分析和比较多尺度图像纹理对岩性分类的作用.结果表明,在岩性分类过程中加入不同尺度的纹理信息可不同程度地提高图像的岩性分类精度,而同时加入多尺度的纹理信息,可得到更高的分类精度.将多尺度的图像纹理信息和光谱信息综合,是一种有效的岩性分类方法.     

一种改进的空间上下文点云分类方法

考虑到点云数据具有线性分布和密度不均匀的特点,以及现有复杂场景点云分类方法中缺少对非局部空间上下文信息的有效利用,提出了一种改进的空间上下文点云分类方法。该方法在提取点云数据顾及曲率的自适应邻域的基础上,首先估算点云局部特征与依赖性空间上下文,并基于超级体素提取分布性空间上下文,最后采用高阶条件随机场模型,实现对点云数据的自动分类,避免了利用单一点云局部特征分类的局限性。试验结果表明,本文方法能够有效提高点云数据地物分类精度。     

合成孔径雷达高度计的波形重跟踪与仿真试验分析

基于初步研究获得的合成孔径雷达高度计卷积模型,推导获得合成孔径雷达高度计波形关于时间偏移、合成上升时间、信号幅度3个参数偏导数的卷积计算公式,利用数值积分及傅里叶变换实现合成孔径高度计回波模型的重跟踪。在多个单位联合协同下,利用仿真轨道、对流层、电离层及潮汐等模型生成了合成孔径模式下的回波波形。对比表明,仿真生成的合成孔径模式下的回波波形与CryoSat-2卫星SAR模式下的回波波形整体形状一致。利用仿真波形数据进行重跟踪试验,结果表明在20Hz数据条件下(约350m分辨率),合成孔径模式下的重跟踪精度达到5cm,较之传统模式有一定的提高。     

特征分类与邻近图相结合的建筑物群空间分布特征提取方法

建筑物群综合过程中需要对建筑物群空间分布特征进行认知和识别。本文在分析国内外相关研究的基础上,从描述建筑物空间特征的大量指标中,利用主成份分析方法,总结并提出了有代表性的建筑物空间特征指标集:凸包面积、紧密度IPQ指标、边数和最小面积外接矩形方向,并基于这些指标研究了建筑物群的分类。在利用最小生成树邻近图(MST)划分建筑物空间子群时,考虑了建筑物成群与所处地理环境(河流和道路等因素)的关系。另外,基于最邻近图(NNG)、MST、相对邻近图(RNG)和Gabriel图(GG)4种建筑物群邻近图,提出了自动识别具有特定空间排列建筑物子群的方法,并比较分析了识别结果的影响因素和可用性。最后,选择北京某地区建筑物群为试验对象,实现了对建筑物群的分类和空间聚类,并提取了其中直线型空间排列的建筑物子群。     

结合对抗网络与辅助任务的遥感影像无监督域适应方法

使用机器学习进行遥感影像标注的一个重要前提是有足够的训练样本,而样本的标注是非常耗时的。本文采用了域适应的方法来解决遥感影像场景分类中小样本量的无监督学习问题,提出了结合对抗网络与辅助任务的遥感影像域适应方法。首先建立了基于深度卷积神经网络的遥感影像分类框架;其次,为了学习到域不变特征,在标签分类器的基础上增加域分类器,并使域损失函数在其反射传播时的梯度与标签损失的梯度相反,从而保证域分类器不能区分样本来自于哪个域;最后引入了辅助分类任务,扩充了样本的同时使网络更具泛化能力。试验结果表明,本文方法优于主流的无监督域适应方法,在小样本遥感影像无监督分类中得到了较好的效果。     

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment,Germany

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.     

基于最优尺度和规则的高分辨率影像分类研究

高分辨率影像的广泛应用推进面向对象影像分析(OBIA)的发展,而分割作为面向对象分类的关键步骤,其尺度的选择直接关系到地物信息的提取。空间尺度是地物的固有属性,在合适的分割尺度下可以更好地挖掘地物信息。本文结合最大面积法和分割质量评价模型对张山营镇影像进行分割实验,先通过分析对象最大面积初步得到最优尺度范围,后结合分割质量评价模型以确定最优分割尺度层次。在此基础上,综合样本提取的光谱、纹理等特征进行规则训练,最终完成面向对象的土地覆被分类研究。结果显示:基于多层次最优尺度的规则分类方法获得更好的分类结果,其总体精度为88.8%,Kappa系数为0.861,而基于单一尺度的最邻近法总体精度81.4%,Kappa系数0.773,基于单一尺度的规则分类法总体精度为83.2%,Kappa系数为0.85。     

基于图形数据差的居民地变化信息提取方法研究

作为空间数据库更新的一项关键内容,变化信息的提取直接关系着整个更新工作的效果。居民地是空间实体中变化频率较高的要素,成为空间数据库更新的关注重点。文中针对当前提取方法尚未关注空间实体具体变化的问题,提出一种基于图形数据差的居民地变化信息提取方法。根据图形数据差的分类,实现变化信息的归类,继而通过图形数据差的判别方法,达到居民地变化信息提取的目的。通过实验验证该方法的可行性和有效性。     

基于GF-1的坑塘信息精确提取方法研究

以国产高分一号2 m全色、8 m多光谱遥感影像作为数据源,选择廊坊市文安县作为研究区域,针对坑塘信息精确提取问题,采用单波段阈值法、监督分类法、面向对象方法等进行实验研究,重点研究了面向对象方法,结合运用遥感影像光谱、纹理、形状等特征,分析坑塘与其他地物之间的差异,最终实现坑塘信息的精确提取。