面向道路网络地图示意化的线形简化方法

针对当前示意网络地图线形状简化方法中存在需要人工干预或地图认知度低的问题,本文提出自动化程度高并能保证良好地图认知的线形状简化方法.该方法通过合理设定线形状简化中所需的阈值以避免人工干预,提高了线形状简化过程的自动化程度,并通过在简化过程中应用平滑算法,以减少示意结果中线对象在方向上的转折数,从而提高了简化度和清晰度.试验表明该方法不仅可以生成清晰的示意结果,并且能够保证良好的地图认知度.     

A spectral gradient difference based approach for land cover change detection

Change detection with remotely sensed imagery plays an important role in land cover mapping, process analysis and dynamic information services. Euclidean distance, correlation and other mathematic metrics between spectral curves have been used to calculate change magnitude in most change detection methods. However, many pseudo changes would also be detected because of inter-class spectral variance, which remains a significant challenge for operational remote sensing applications. In general, different land cover types have their own spectral curves characterized by typical spectral values and shapes. These spectral values are widely used for designing change detection algorithms. However, the shape of spectral curves has not yet been fully considered. This paper proposes to use spectral gradient difference (SGD) to quantitatively describe the spectral shapes and the differences in shape between two spectra. Change magnitude calculated in the new spectral gradient space is used to detect the change/no-change areas. Then, a chain model is employed to represent the SGD pattern both qualitatively and quantitatively. Finally, the land cover change types are determined by pattern matching with the knowledgebase of reference SGD patterns. The effectiveness of this SGD-based change detection approach was verified by a simulation experiment and a case study of Landsat data. The results indicated that the SGD-based approach was superior to the traditional methods.     

一种基于对象和多种特征整合的分类识别方法研究

遥感图像空间分辨率的提高，为目标物的纹理特征和形状特征的提取提供了客观基础，同时也使得传统的基于像元的分类识别方法受到了严重的挑战。因此，需要对传统的方法进行改进或发展新的方法。本文采用面向对象的分析思想，通过图像分割和分割对象的矢量化等一系列的预处理，并在此基础上实现了目标形状信息的提取，最后综合利用光谱特征和形状特征应用模糊分类器实现两种典型的人造目标的分类提取实验。识别的精度评价主要通过目视解译完成。分析表明，形状信息的提取大大丰富了目标识别的特征库，尤其在感兴趣目标与背景物具有相近的光谱反应而形状特征有明显差异的条件下，这种利用光谱与形状特征整合的提取方法能够大大提高目标的识别精度。     

改进霍夫变换的枢纽立交桥检测方法

针对传统遥感系统对特定建筑物提取难的问题,该文提出了一种高空摄影系统下基于改进霍夫变换的城市枢纽立交桥检测方法。该方法首先对图像进行预处理和分割,对分割后图像进行形态学处理提取直线和曲线道路,弥补了单一提取直线道路的不足;然后对提取后的道路进行基于改进霍夫变换的近似圆形立交桥匝道的识别;最后依据圆形完整度门限判断可识别的圆形匝道个数,并根据识别数目来判定是否存在立交桥。经过实验比较分析,该方法对于国内外常规的立交桥系统识别效果都较好。     

Deriving airborne laser scanning based computational canopy volume for forest biomass and allometry studies

A computational canopy volume (CCV) based on airborne laser scanning (ALS) data is proposed to improve predictions of forest biomass and other related attributes like stem volume and basal area. An approach to derive the CCV based on computational geometry, topological connectivity and numerical optimization was tested with sparse-density, plot-level ALS data acquired from 40 field sample plots of 500–1000 m² located in a boreal forest in Norway. The CCV had a high correspondence with the biomass attributes considered when derived from optimized filtrations, i.e. ordered sets of simplices belonging to the triangulations based on the point data. Coefficients of determination (R²) between the CCV and total above-ground biomass, canopy biomass, stem volume, and basal area were 0.88–0.89, 0.89, 0.83–0.97, and 0.88–0.92, respectively, depending on the applied filtration. The magnitude of the required filtration was found to increase according to an increasing basal area, which indicated a possibility to predict this magnitude by means of ALS-based height and density metrics. A simple prediction model provided CCVs which had R² of 0.77–0.90 with the aforementioned forest attributes. The derived CCVs always produced complementary information and were mainly able to improve the predictions of forest biomass relative to models based on the height and density metrics, yet only by 0–1.9 percentage points in terms of relative root mean squared error. Possibilities to improve the CCVs by a further analysis of topological persistence are discussed.     

线要素几何信息量度量方法

目的 提出了基于弯曲的线要素几何信息量的度量方法。首先利用拐点识别线要素弯曲,将线要素几何结构分解为一组有序弯曲。进而基于地图信息产生的差异性和多样性特征本质,从元素层次的弯曲形态、邻域层次的弯曲拓扑和整体层次的弯曲分布来描述弯曲序列的空间特征,并提出相应的几何信息分类,即弯曲几何形状信息、弯曲几何拓扑信息和弯曲几何分布信息。然后针对不同类型的几何信息,分别建立各层次空间特征描述指标,在此基础上提出相应的信息量度量方法。实验验证了本文方法的可行性和合理性。     

一种多系统组合导航快速选星方法

采用多卫星导航系统组合导航,定位精度和系统可靠性会大幅提升,但导航定位运算量也会成倍增长。为解决多系统组合导航定位精度与实时性之间的矛盾,提出一种新的选星方法。新方法不追求最小GDOP值,而是以满足导航定位精度的GDOP值为前提,结合模糊理论中隶属函数的思想,按卫星在星座中均匀分布为原则进行选星。推导伪距测量的误差模型,分析了GDOP与测量误差之间的关系。北斗、GPS和GLONASS三系统组合导航选星实验结果表明,在不超过3次求解GDOP值的情况下,新方法能以不小于98%的概率得到GDOP≤4。     

AlexNet支持下的地图建筑物形状分类方法

地图目标的形状在地图制图综合、空间查询等研究中发挥着重要作用。地图建筑物形状的识别与分类作为建筑物轮廓化简与典型化的基础,一直是制图综合研究的热点问题。目前,主要的建筑物形状识别方法主要依赖对建筑物轮廓的描述,对建筑物等地图面状要素的形态特征有较强的依赖性,通常只在应对特定类型的规则轮廓或直角化轮廓时能发挥较好的效果,对于形状不规则或复杂的情况识别不佳。本文提出一种AlexNet支持下的地图建筑物形状分类方法,将矢量地图中建筑物数据的形状分类问题,转化为建筑物栅格图像的分类问题,通过完成卷积神经网络的图形分类实现建筑物的形状识别。该方法首先结合空间认知规律提出一系列典型建筑物形状类型,然后利用矢量-栅格转换的方法从OSM数据采样单体建筑物栅格图像,通过人工标识获得建筑物形状分类训练样本,训练AlexNet卷积神经网络分类模型,最后利用训练好的模型对大比例尺建筑物数据进行智能形状分类与识别。本文利用北京、香港2个城市的OSM建筑物数据作为样本训练建筑物形状分类模型,并在广州部分城区的OSM建筑物数据上进行验证。相较传统形状相似性度量方法,本文提出的方法对实验区建筑物的识别分类总体查全率提...