基于 ObjectArx 技术的横断面自适应提取

为降低水利、公路等线路工程中横断面提取的复杂度,本文针对横断面提取的特点和专业要求,结合不同行业应用需求对横断面自适应提取进行了分析和研究,给出了涉及的关键算法和数学模型。在AutoCAD环境下基于ObjectARX技术实现了多种数据源的横断面自适应提取。项目生产实践表明,该方法显著地降低了劳动强度,提高了生产效率。     

顾及方向关系的农村居民地聚类方法

农村居民地空间分布具有独特的规律性和复杂性,Voronoi图在表达居民地分布特征方面有显著优势。针对当前空间聚类较少考虑实体方向关系的问题,基于Voronoi图提出一种顾及方向关系的农村居民地聚类方法。首先,构建距离约束的Voronoi图,并构建居民地实体间的Voronoi邻近图;然后,利用无向特征与有向特征来综合评价居民地实体间的聚集强度;最后,消除聚集强度小于阈值的实体对的邻近关系,得到聚类结果。采用浙江省宁波地区部分农村居民地数据进行实验,结果表明,所提方法能够有效聚类不同分布模式的居民地,聚类结果符合人的认知习惯。     

基于误差补偿预测树的多光谱遥感图像无损压缩方法

预测树方法是一种有效的无损多光谱图像压缩技术,将自适应线性预测方法与传统预测树方法相结合,提出了一种多光谱遥感图像的误差补偿预测树压缩方法。该方法利用多光谱图像谱间的局部统计冗余和结构冗余建立自适应预测器,对传统预测树方法产生的误差进行补偿,从而进一步减少了多光谱图像的数据量;并且利用多光谱图像的局部平稳特性对算法进行了简化。实验结果表明,该方法得到的压缩比与原始预测树方法相比有明显提高,同时算法简化后可以使计算复杂度大幅度降低。     

Visualizing uncertainty — How fuzzy logic approach can help to explore iron ore deposits?

Uncertainties in Geovisualaization / GIScience spatial data can minimize but not completely provided by the different image processing classification methods. The methods of image processing techniques are purely dependent on spectral signature values. In the present study, we collected end member spectral values from both satellite data and field signatures and applied in supervised and fuzzy classification of image processing techniques to discriminate the iron ore formations and associated land cover features of part of Godumalai hill region of Salem District, Tamil Nadu State, India. The result of analysis shows that the fuzzy classified image discriminated the iron formation with better appearance and distinct boundary between the associated features than the analyses results obtained by supervised methods.     

结合引力场理论的道路自动选取方法

针对复杂网络模型被广泛应用于道路选取的研究中,少有方法顾及道路网对偶图中多级邻居节点的影响,导致对道路的重要性评价缺乏准确性和可靠性的问题。该文将道路网对偶图节点的结构特征值视作质量,将节点间的最短距离视作距离,结合引力场方程实现道路自动选取,较好地将道路网对偶图中多级邻居节点的影响纳入到道路重要性的计算中,从而实现对道路重要性的准确评估。通过对兰州市城关区的路网进行实验,结果表明本文方法所选取的路网较好地保持了原始道路网的整体结构、覆盖范围、密度分布、拓扑特征和连通性。     

城市房价空间分布及其影响因素分析

针对城市房价的空间分布规律及其影响因素的研究,该文提出了以南昌市青山湖区房价为研究对象,基于相关理论,搜集整理了2015年07月到10月南昌市青山湖区155个楼盘的均价,利用市场比较法把房价修正到2015年10月份节点上,估算出了155个楼盘点的价格,以GIS技术为研究平台,运用普通克里格插值方法,得到了青山湖区房价的等值线图,根据等值线图得到其空间分布情况,从可达性视角出发,采用结构方程模型构建了青山湖区房价影响因素分析框架,运用SPSS分析出各自变量和因变量之间的关系,即定量分析出了各影响因素对房价格产生的影响程度。     

Exploration of Information Theoretic Arguments for the Limited Amount of Information in a Map

From the application of information theory capacity limits of maps are derived and some cartographic rules are formulated—based on theoretical models and perception studies. The perception study shows that for a relief map with coloured height intervals the channel capacity, as defined in information theory, is reached at seven～eight height classes. Generally, the length of a visual variable may be characterized by the channel capacity of the map. This theoretical study shows how increased attention should be paid to perceptual separation as the number of categories increases. The requirement becomes more and more crucial as the number of categories exceeds five. The principle of group visibility is formulated and demonstrated. Group visibility considers the visibility of groups of map features and may be utilized in a visual search for geographical patterns. The quantitative measures presented offers computational methods to control group visibility for example in an interactive visualization system.     

基于EKF和自适应抗差滤波的星载GPS定轨方法

针对在星载GPS卫星定轨中由于卫星动力学模型误差和不可避免的观测异常严重影响定轨精度的问题,通过采用适当的自适应控制因子和应用抗差估计原理,构造自适应抗差扩展卡尔曼滤波(RAEKF)来实现星载GPS卫星定轨。实测计算表明,自适应抗差扩展卡尔曼滤波对观测误差和状态扰动有一定的抵制能力,与一般扩展卡尔曼滤波相比提高了精度,证明其理论的可行性。     

Auto-detection and integration of tectonically significant lineaments from SRTM DEM and remotely-sensed geophysical data

A set of techniques was developed for automatically detecting tectonic lineaments from multi-source remotely-sensed data at various scales. The techniques include adaptive shading of grid data to enhance linear features, a segment-tracing algorithm to extract line segments from the shaded grid data, grouping of the segments by concatenating short segments, and connecting them by proximity and co-linearity criteria to form a lineament that represents significant tectonic structure. B-spline smoothing was adopted for lineament representation. Finally, a technique for assessing the orientations and styles of faulting (normal, reverse, and strike-slip types) was developed for use in characterizing the extrapolated fracture planes. The applicability of the developed techniques was examined using 30 arc-second topography/bathymetry grids, 1-min gravity anomaly grids, and 2-min total field magnetic intensity grids covering Egypt and its surroundings. Lineaments derived from data types so diverse in composition and from various depths corresponded well with the referenced tectonic features over much of the region. Prominent trends and faulting styles of lineaments provided important clues as to the timing of their development as well as strong support for a structural inheritance model. Results demonstrated the effectiveness of the developed techniques combined with integration of remotely-sensed data in detecting regional fracture systems accurately and in characterizing geodynamics over a long timeframe.     

顾及制图规则的地图多要素协同移位方法

在地图自动编制中,有时多类地图要素多个空间目标之间同时发生空间冲突,在移位过程中会影响到邻近区域的地图目标,这就需要对地图要素移位进行协同处理。在自动识别邻近空间冲突区的条件下,提出了一种顾及地图制图规则的解决地图目标群空间冲突的协同处理方法,并用参数表方法形式化表达了地形图上地图要素移位的常用制图规则。首先利用约束性Delaunay三角网识别出可能的空间冲突区域,并建立移位操作区;然后,建立移位传播关联线网,依据制图规则在关联线网上以能量最小化Beam移位模型为基础,对整个空间冲突区的地图目标群进行协同移位;最后,用实验验证了所提方法的有效性和适用性。     

基于非线性高斯-赫尔默特模型的结构总体最小二乘法

变量误差（error-in-variables,EIV）模型的系数矩阵存在结构特征的情况,并且这种结构特征可以扩展到观测向量中。首先采用变量投影法将系数矩阵的增广矩阵展开成仿射矩阵形式,提取系数矩阵和观测向量中的随机量,并将EIV模型表示为非线性高斯-赫尔默特模型,然后利用非线性最小二乘原理推导了一种结构总体最小二乘法。该算法统一了普通的结构总体最小二乘法、结构数据最小二乘法以及最小二乘法。将该算法应用到真实算例和模拟算例中,两个算例结果表明,该算法与已有能够解决EIV模型结构特征的结构或加权总体最小二乘法估计结果一致,验证了该算法的有效性。同时,该算法对结构特征的提取方式简单、规律性强且易于编程实现;且在算法设计中,把结构总体最小二乘问题转换为附有参数的条件平差问题,即将其纳入到最小二乘平差理论体系,便于其扩展应用。同时对平面拟合问题的误差估计特性进行了定性分析,由分析可知参数的相对大小对估计误差的一致性有直接影响,这说明EIV模型下系数矩阵和观测向量中随机量的估计误差与真误差的一致性关系相对复杂。     

高光谱图像相对含水量反演引导的精细分类

深度学习在高光谱图像处理领域的研究应用不断深入发展,基于深度学习的高光谱图像分类达到了较高的分类精度。目前的分类模型多利用高光谱的图谱特征,但对光谱的诊断性特征及先验信息利用不足,对空谱特征提取过程难以实现有效协同,因而导致分类类别即类内分类不够精细。为了解决以上问题,本文提出一种以多标签数据为输入的共生神经网络模型,在高光谱图谱特征提取的基础上融合光谱诊断特征,实现相对含水量反演及精细分类。首先,通过构建一种新的红边斜率光谱指数实现高光谱图像相对含水量的表征,利用本文提出的自适应分级算法完成相对含水量反演并建立对应的等级标签,与地物种类标签共同构成多标签高光谱数据集。然后,构建共生神经网络架构及内部变维特征提取模块,利用多标签数据提取高光谱图像中空间、光谱和相对含水量的融合特征,提高深度模型对不同含水量地物的区分能力和对所提取特征的协同表达能力,降低模型的复杂度与计算量,完成基于多标签数据集的相对含水量反演引导分类的过程,在扩大传统类间距离的基础上进一步扩大类内距离,从而实现高光谱图像的精细分类。最后,使用实验室采集数据和4个公开的高光谱数据集Lopex、Indian Pines、Pavia University和Salinas进行实验验证。结果表明,本文提出的红边斜率光谱指数可以有效表征地物的相对含水量信息;相对含水量反演引导的分类模型对类内分类精度有较明显的提升,对总体分类结果有一定的改善;与其他机器学习和深度学习分类算法相比,本文算法取得了较好的分类结果,提高了深度分类模型的分类性能和精细程度,实现了精细分类。     

The geology and structure of the area south of Gani,Kurnool District,Andhra Pradesh — a study based on remotely sensed data

Geological investigations in the area south of Gani, Kurnool District, Andhra Pradesh, were carried out with the help of aerial Photographs and multiband satellite imageries. The rocks of the Cuddapah Group and the Kurnool Group are exposed in the area under study. The Gani-Kalava anticline and the ENE-WSW trending Kalava fault are the important structural features of the area. The anticlinal structure could be better studied using the aerial photographs, while the fault is clearer on the imagery. Small scale folds involving the Cuddapahs and the Kurnools along the northern limb of the fold could be mapped using the aerial photographs. The Gani-Kalava anticline is a post-Kurnool structure, formed by movement along the Kalava fault and in the first instance the minor folds appear to be drags on the northern limb. However, certain structural and stratigraphic relations suggest that the folding in the Cuddapahs may be a pre-Kurnool phenomenon.     

多特征证据融合的遥感图像变化检测

提出了一种以证据理论综合利用图像多种特征的变化检测方法。方法利用滑动窗口计算两时相图像3种特征的结构相似度, 以之构建D-S证据理论的基本概率赋值函数并进行证据合成, 通过规则判定得到图像变化区域。通过对不同试验区、不同证据组合方式以及方法间的比较实验表明, 相对单一特征检测方法有效地提高了检测的精度。此外, 由于采用统计而非原始图像特征度量特征相似性, 方法具有对辐射、几何配准精度要求较低等优点。     

Adaptive fitting of systematic errors in navigation

To use Kalman filtering for kinematic positioning and navigation, we have to deal with both observational and kinematic models. Both of the functional models may contain global or local systematic errors. The influence functions of the systematic errors on the estimates of kinematic states are derived. An adaptive fitting method for systematic errors of the observations and kinematic model errors is presented. The systematic errors are fitted with a mean or a weighted mean by using the residuals of observations and residuals of predicted states within a chosen time window. The covariance matrices of the modified observations and the predicted states are estimated within the same window. The estimation formulae and calculation strategy, as well as a real example, are given. It is shown by theory and calculations that Kalman filtering based on the adaptive fittings of the systematic errors and covariance matrices can, to some degree, resist the influences of systematic errors on the estimated states of navigation.     

IMD法在多光谱图像分类中的应用

数学形态学着重于分析和处理图像的几何结构信息。根据这一原理，在图像分类预处理过程中，对各种地物区域的几何结构特点进行分析，构造相应的结构元素，然后对图像进行形态迭代分解法(IMD，Iterative Morphological Decompostion)变换。在变换结果中，地物区域的灰度信息进行了归一化处理，同时区域的几何结构特点和独立地物得到了有效保留。将多光谱图像的IMD变换结果进行分类实验的结果表明，该方法可以有效提高多光谱图像分类的精度和效率，具有较强的适用性。     

集成地理探测器与随机森林模型的城市人口分布格网模拟

精细尺度的城市人口分布信息是城市资源配置和综合管理的重要依据。本文以广州市越秀区、荔湾区、天河区、海珠区、白云区及黄埔区作为研究区域,基于人口统计、夜间灯光、兴趣点及土地利用等多源数据,利用地理探测器识别人口分布的影响因子,运用随机森林模型开展人口分布空间格网模拟研究。研究结果表明,与传统的相关分析相比,地理探测器能够更为准确地识别人口空间分布的重要影响因子。基于随机森林模型的人口分布格网模拟结果与街道（镇）实际人口的相关系数为0.774,平均相对误差约为30%。相比基于线性回归模型的模拟结果,随机森林模型的精度有明显提高。     

基于区域平均垂直不确定度的自适应网格水深建模方法

提出一种基于区域平均垂直不确定度的自适应网格数字水深模型构建方法。利用水深不确定度及其传递机制,计算建模区域的水深平均不确定度,自动确定网格剖分层次和组织水深数据;并在公共边界处增加约束条件,解决相邻网格区域的水深模型不连续问题。试验证明:①在水深变化趋势不一致的区域,与等间距法和水深复杂度法相比,不确定度法所构DDM具有更高的质量;②相对于等间距法,不确定度法能自动确定网格剖分层次和网格大小,控制模型质量达到预先设计的指标要求,相对水深复杂度法,具有更强的自适应调控能力;③不确定度法比水深复杂度法具有更高的自动化程度和效率。     

Robust adaptive filter for shipborne kinematic positioning and velocity determination during the Baltic Sea experiment

Information on trajectory and attitude is essential for analyzing gravimetric data collected on kinematic platforms. Usually, a Kalman filter is used to obtain high-accuracy positional and velocity information. However, this can be affected by measurement outliers and by state disturbances that occur frequently under a fast-changing environment. To overcome these problems, a robust adaptive Kalman filtering algorithm is applied for state estimates, which introduces an equivalent weight to resist measurement outliers and an optimal adaptive factor to balance the contributions of the kinematic model information and the measurements. In addition to the conventional robust estimator, an improved Current Statistical (CS) model is proposed. The improved CS model adopts a variance adaptive learning algorithm, and it can perform self-adaptation of acceleration variance with the innovation information; thus, it can overcome the shortcoming of lower tracking accuracy and avoid setting the maximum acceleration. Following a gravimetry campaign on the Baltic Sea, it is shown in theory and in practice that the robust adaptive Kalman filter is not only simple in its calculation but also more reliable in controlling the colored observation noise and kinematic state disturbance compared with the classical Kalman filter. The improved CS model performs best, especially when analyzing the positioning errors at the turns due to the target maneuvering. Compared to the CS model, the RMS values of the positional estimates derived from the improved CS model decrease by almost 30% in the horizontal direction, and no significant improvement in the vertical direction is found.     

Measuring the influence of map label density on perceived complexity: a user study using eye tracking

ABSTRACT

We combine eye tracking and a questionnaire-based approach to explore the influence of label density on the perceived visual complexity of maps. We design two experiments in which participants are asked to search for the names of point features on maps and to rate the map complexity and legibility for different label densities. Specifically, we conduct a highly controlled experiment in which all the map variables except the label density are held constant (the controlled experiment). Then, we conduct a second experiment following the same protocol but using real maps as visual stimuli (the real-map experiment) to verify if the results of the controlled experiment were applicable to real maps. The results of both experiments indicate a significantly positive correlation between perceived visual complexity and label density and between the response time in visual search tasks and label density. Surprisingly, we observe a significant inverse correlation between the label density and two eye movement parameters (fixation duration and fixation frequency) between the two experiments. We discuss how the variables of real maps might have affected these eye movement parameters and why the results of the two experiments are inconsistent. Our findings suggest that eye tracking parameters are not reliable indicators of map complexity. These empirical results can be helpful to future map design and map complexity investigation.