基于动恣规划的整体影像匹配

影像匹配是数字摄影测量工作的基础。考虑到多条核线上的匹配点，本文提出了一种基于动态规划的整体影像匹配方法，构造了一种新的立体匹配模型，并将该模型转化成为简单的一维动态规划模型，采用对角线的方法划分阶段，用视差较和相关系数来构造阶段的代价函数，从而得到了基于动态规划的整体最优匹配结果。     

江苏南部地区煤层气资源

根据江苏南部地区二叠系龙潭组煤系的分布和规模、煤矿瓦斯涌出特征、煤的产气能力、煤层的储集条件等煤层气地质条件综合研究工作，认为该区煤层气资源存在一定的勘探开发前景，对各典型含煤区块煤层气综合评价出有远景区（A类）、较有远景区（B类）和远景差区（C类）。     

华北地区震群活动的阶段性特征

通过对华北地区1970年以来震群活动的分析表明，唐山地震的震群活动显示出一定的迁移性和阶段性。应用流变介质包体孕震模型的理论进行了初步力学分析。张北地震的预测检验表明，震群活动的迁移性与阶段性对强震的中期预测具有一定指导意义。     

1979年加州凯奥蒂湖地震序列震源机制分析:美国加州地震比中国大陆地震破裂过程简单的证据

本文详细分析了1979年美国加利福尼亚凯奥蒂湖地震序列在时间和空间之中发展的非常有序的过程.对凯奥蒂湖地震序列,在三种情况下分别做出34次地震的断层面解.分析了美国地质调查局观测报告中地震初动的可信性.论证了伯克利加利福尼亚大学的地壳模型与美国地质调查局的地壳模型对于求断层面解的等效性.与唐山地震序列相比,凯奥蒂湖地震序列表现出4个主要特点:1.震中分布呈规则的狭长条带,与断层的关系密切;2.断层面解中出现的矛盾初动少;3.断层面解之间的一致性好;4.断层面解随时间没有明显的变化.这些都表明,凯奥蒂湖地震序列比唐山地震序列特征简单,前者基本上是沿卡拉维拉斯断层发生的面破裂,而后者却是在一定震源区中发生的体破裂.唐山地震序列的特征在中国大陆是有代表性的.若凯奥蒂湖地震序列的这些特点,在加州也具有代表性,则这两个地震序列的差别,可以作为美国加州地震比中国大陆地震发生环境和发生过程都简单的一个证据.     

大春河—一朵云断裂断层泥中石英表面 SEM 特征的分析研究

大春河—一朵云断裂位于昆明市的东南侧,沿断层线断错水系、土林地貌发育,河流的最大左旋扭动距离达两公里。断层泥中石英碎砾的SEM研究表明:断层的强烈活动时期为早更新世晚期和晚更新世,全新世断层的活动有所减弱;晚更新世以来断裂的活动方式以蠕滑为主。     

Automatic registration of aerophotos based on SUSAN operator

The technique of image matching is the basis of image fusion,image mosaic and scene 3D reconstruction.In the paper a novel image registration method based on SUSAN operator is proposed.Firstly,Corner points are extracted by using SUSAN(Smallest Univalue Segment Assimilating Nucleus) operator.Then matched corner points are selected through coarse matching and fine matching.Based on such corner pairs,aerophotos are registered automatically.     

Age of Berh Hairhan Formation of Mongolia dated by conodonts

Conodont Caudicriodus angustus angustus(Steward et al.,1956)and Caudicriodus angustus cauda(Wang et al.,2005)were found from a big limestone lens of the Berh Hairhan Formation in the place of W 150 km to Ulaanbaatar City.The discovery indicates that the Berh Haihan Formation should be assigned to Middle Devonian Eifelian Stage.This conodont fauna was from a shallow cooler water facies in the relatively high latitude.     

An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-sections

The characterisation the vertical profiles and cross-sections of roads is important for the verification of proper construction and road safety assessment. The goal of this paper is the extraction of geometric parameters through the automatic processing of mobile LiDAR system (MLS) point clouds. Massive and complex datasets provided by the MLS are processed using a hierarchical strategy that includes segmentation, principal component analysis (PCA)-based orthogonal regression, filtering and parameter extraction procedures. Best-fit geometric parameters act as a vertical road model for both linear parameters (slope and vertical curves) and cross-sections (superelevations). The proposed automatic processing approach gives satisfactory results for the analysed scenario.     

A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas

In recent years, it has been widely agreed that spatial features derived from textural, structural, and object-based methods are important information sources to complement spectral properties for accurate urban classification of high-resolution imagery. However, the spatial features always refer to a series of parameters, such as scales, directions, and statistical measures, leading to high-dimensional feature space. The high-dimensional space is almost impractical to deal with considering the huge storage and computational cost while processing high-resolution images. To this aim, we propose a novel multi-index learning (MIL) method, where a set of low-dimensional information indices is used to represent the complex geospatial scenes in high-resolution images. Specifically, two categories of indices are proposed in the study: (1) Primitive indices (PI): High-resolution urban scenes are represented using a group of primitives (e.g., building/shadow/vegetation) that are calculated automatically and rapidly; (2) Variation indices (VI): A couple of spectral and spatial variation indices are proposed based on the 3D wavelet transformation in order to describe the local variation in the joint spectral-spatial domains. In this way, urban landscapes can be decomposed into a set of low-dimensional and semantic indices replacing the high-dimensional but low-level features (e.g., textures). The information indices are then learned via the multi-kernel support vector machines. The proposed MIL method is evaluated using various high-resolution images including GeoEye-1, QuickBird, WorldView-2, and ZY-3, as well as an elaborate comparison to the state-of-the-art image classification algorithms such as object-based analysis, and spectral-spatial approaches based on textural and morphological features. It is revealed that the MIL method is able to achieve promising results with a low-dimensional feature space, and, provide a practical strategy for processing large-scale high-resolution images.