数字河流数据的自动综合

本文主要介绍了数据库中水系要素综合中河流的自动综合方法，尤其是对非结构化河网中河流的自动选取作了较为详细的论述，并通过数据库中一幅完整图幅的河流数据对文中介绍的自动综合方法进行了验证。     

军事交通图数据库支持下的自动制图综合研究

本文以1:50万军事交通图数据库为基础,探讨并研究了数据库支持下的自动制图综合的理论,方法和实施过程:重点研究了地图内容的选取、线状要素的化简和重迭要素的移位问题;尤其是对图形要素的移位,做了较为详细的分析,提出了检查线状要素之间图形重迭的方法及自动移位的具体措施,并给出了用于程序运行的一些详细算法。     

面向四维变分资料同化的NSAS积云深对流参数化方案的简化及线性化研究

GRAPES全球四维变分资料同化系统需要积云深对流参数化方案的线性化与伴随方案,直接采用原始复杂参数化方案进行线性化并不可行,需要发展简化光滑方案来减缓非线性与非连续性特征。GRAPES全球模式采用NSAS积云对流参数化方案,积云深对流对环境的反馈主要通过补偿下沉来实现,研究突出补偿下沉作用,忽略降水蒸发、动量反馈等贡献,形成简化方案。采用输入温、湿度廓线加入不同幅度小扰动方法,评估参数化方案计算的温度、比湿时间倾向对输入扰动的敏感性,检验非线性与非连续特征。提出避免或减缓非连续“开关”的方法,在简化方案的基础上发展了简化光滑方案。简化光滑方案与原始积云深对流方案相比,在对流触发上一致,在对流的位温与比湿倾向、降水的时序模拟等方面相似,而在减缓非线性、避免非连续性方面显著优于原始方案。基于简化光滑方案发展的线性化方案表明,对小于2倍分析增量幅度的扰动,线性化方案可以较好地模拟非线性方案的扰动发展。发展的简化光滑方案具有合理性和实用性。      

An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds

An algorithm for automatic extraction of pole-like street furniture objects using Mobile Laser Scanner data was developed and tested. The method consists in an initial simplification of the point cloud based on the regular voxelization of the space. The original point cloud is spatially discretized and a version of the point cloud whose amount of data represents 20–30% of the total is created. All the processes are carried out with the reduced version of the data, but the original point cloud is always accessible without any information loss, as each point is linked to its voxel. All the horizontal sections of the voxelized point cloud are analyzed and segmented separately. The two-dimensional fragments compatible with a section of a target pole are selected and grouped. Finally, the three-dimensional voxel representation of the detected pole-like objects is identified and the points from the original point cloud belonging to each pole-like object are extracted.The algorithm can be used with data from any Mobile Laser Scanning system, as it transforms the original point cloud and fits it into a regular grid, thus avoiding irregularities produced due to point density differences within the point cloud.The algorithm was tested in four test sites with different slopes and street shapes and features. All the target pole-like objects were detected, with the only exception of those severely occluded by large objects and some others which were either attached or too close to certain features.     

Geometric structure simplification of 3D building models

The complexity of building models directly affects the application efficiencies of 3D urban maps. To address the challenges of building models with various structures, we propose a structural simplification method in this paper. The geometric structures of building models are classified into three categories: embedded structures, compositional structures, and connecting structures, which can be extracted separately through convex/concave analysis. Some specific rules are proposed for the simplification of geometric structures, and the building models are suggested to be simplified progressively. The robustness and efficiency of the method are demonstrated by experiments, and the applications of the Levels of detail of the building models are illustrated.     

多进制小波及其在DEM数据有损压缩中的应用

多进制小波是近几年刚发展的小波理论的一个新的分支,本文阐述了多进制小波分析的基本方法及其较之于二进制小波的若干优点,提出利用多进制小波变换压缩（简化）ＤＥＭ数据的方法,并给出实验结果。     

Object-based image analysis through nonlinear scale-space filtering

In this research, an object-oriented image classification framework was developed which incorporates nonlinear scale-space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. Image objects were computed at various scales and were connected to a kernel-based learning machine for the classification of various earth-observation data from both active and passive remote sensing sensors. Unlike previous object-based image analysis approaches, the scale hierarchy is implicitly derived from scale-space representation properties. The developed approach does not require the tuning of any parameter—of those which control the multi-scale segmentation and object extraction procedure, like shape, color, texture, etc. The developed object-oriented image classification framework was applied on a number of remote sensing data from different airborne and spaceborne sensors including SAR images, high and very high resolution panchromatic and multispectral aerial and satellite datasets. The very promising experimental results along with the performed qualitative and quantitative evaluation demonstrate the potential of the proposed approach.     

Simultaneous curve simplification

In this paper we present a method for simultaneous simplification of a collection of piecewise linear curves in the plane. The method is based on triangulations, and the main purpose is to remove line segments from the piecewise linear curves without changing the topological relations between the curves. The method can also be used to construct a multi-level representation of a collection of piecewise linear curves. We illustrate the method by simplifying cartographic contours and a set of piecewise linear curves representing a road network.      

A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data

The split-window algorithm is the most commonly used method for land surface temperature (LST) retrieval from satellite data. Simplification of the Planck’s function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck’s radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck’s function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the LST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the LST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between LST from MODIS LST product and LST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust; 2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS LST product; and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS LST product. We conclude that the RBSWA for LST retrieval from MODIS data can attain a better accuracy than the BTBSWA.     

Dominant processes concept,model simplification and classification framework in catchment hydrology

Technological and methodological advances have facilitated tremendous growth in hydrology during the last century; however, there are also concerns that these advances indirectly contribute to additional problems in our research. An insight into hydrologic literature reveals our tendency to develop more complex models than perhaps needed, and our increasing emphasis on individual mathematical techniques rather than general hydrologic issues. Some recent studies of diverse forms have suggested that simplification in modeling and development of a common framework may help alleviate these problems. The present study is intended to bring such studies together towards a more coherent approach to research in catchment hydrology. This is done by highlighting the need for model simplification and generalization and proposing some potential directions for achieving such. Through a discussion of difficulties in data measurements, the need for moving beyond the notion of “modeling everything” to the notion of “capturing the essential features” is explained; the concept of dominant processes in model simplification and the utility of integration of concepts for modeling improvement are discussed. Formulation of a catchment classification framework is advocated as a possible means for a common framework in hydrology, and the role of dominant processes in this formulation is presented; the problems due to adoption of different modeling terminologies are highlighted and potential ways to overcome such are also discussed.