基于神经网络的地图数字注记识别

指出了地图自动识别系统中地图数字注记识别存在的困难，论证了利用神经网络技术解决这种困难的可能性，并通过一个含有２个隐藏层的ＢＰ网络，说明了这种技术用于地图数字注记识别的可行性。     

模糊联想记忆神经网络及其在地图数字注记识别中的应用

地图上数字注记的自动识别是实现全要素图纸读取的关键技术之一。本文分析了传统的光学字符识别方法和ＢＰ神经网络识别方法在识别时存在的种种困难，提出了一种基于模糊联想记忆神经网络的识别方法，并以初步实验证实了这种方法的有效性。     

模糊联想记忆神经网络及其在地图数字注记识别中的应用

地图上数字注记的自动识别是实现全要素图纸读取的关键技术之一，本文分析了传统的光学字符识别方法和ＢＰ神经网络识别方法在识别时存在的种种困难，提出了一种基于模糊联想记忆神经网络的识别方法，并以初步实验证实了这种方法的有效性。     

地图数字注记识别的神经网络方法

讨论了基于径向基函数网络（RBFN）的地图数字注记识别问题。在分析了网络结构和训练算法的基础上,用地回扫描数据进行了识别实验。结果表明,本文提出的方法用于地图数字注记识别是非常有效的。     

一种彩色栅格地图注记识别方法

栅格地图中的注记包含了丰富的地理位置信息,传统的栅格地图注记识别方法在识别过程中将注记和其对应的地理要素相分离,因此难以处理倾斜注记和弯曲注记。以连通域单元为基本处理对象,提出了一种基于几何特征的彩色栅格地图注记识别方法。首先利用连通域单元的方向、尺寸、密度、邻域四个特征提取注记像素集合,然后通过地理要素中心线对非水平注记进行方向确定,最后对注记进行聚类和重新排列,完成注记识别。通过对GoogleMap（谷歌地图）瓦片中的注记进行提取和识别实验,证明了该方法的合理性和有效性。     

地图注记的可视化及其语音阅读

本文论述了地图注记可视化的重要性，探讨了如何将地图数据库的注记信息在屏幕上实现可视化的技术方法，以及在注记可视化的实现过程中需要注意的若干问题，包括注记的坐标转换、注记的大小、注记的字型等；论文还提出了地图注记语音阅读的解决办法。     

基于多目标进化算法的地图注记自动配置技术的研究

在计算机数字制图过程中,地图注记的配置直接影响数字地图的效果.针对地图注记配置过程中两个或多个注记之间冲突处理的问题,本文参照格式塔原则,从地图注记的易读性、位置优先性、形状相似性、地物关联性四个格式塔因子出发对地图注记的候选位置进行评价;同时根据多目标决策理论,用进化算法模拟地图注记的自动配置过程,实现了地图注记自动配置的全局优化.     

地图注记设计若干问题的探讨

地图通过注记来标注识别地物,选择字体、确定字大是地图设计工作的重要内容,阐述了注记对地图的重要性;介绍了地图注记的字体、字大、字色等方面的设计;详细介绍了字体设计的几种原则和注记大小几种单位间的转换关系;探讨了影响注记色彩设计的几个因素。     

地图汉字注记的自动定位研究

注记是地图的重要组成部分，注记位置、方向选择恰当与否，与地图的易读性和使用价值有密切关系，注记速度也影响着地图的成图周期。可是注记效果不理想，注记速度慢却是计算机制图中的常见现象，即使是在计算机制图技术有了很大发展的今天，自动注记仍然没有得到很好解决。最近的研究表明，找到具有最佳效果的注记从时间上来讲是不可能的，因而自动注记算法都是希望在尽量短的时间内，得到尽可能好的注记效果。自动注记的主要难点在于自动定位。本文针对不同的地图要素，提出了基于回溯的自动定位算法，取得了较好的效果     

地图汉字注记的自动定位研究

注记是地图的重要组成部分，注记位置、方向选择恰当与否，与地图的易读性和使用价值有密切关系，注记速度也影响着地图的成图周期。可是注记效果不理想，注记速度慢却是计算机制图中的常见现象，即使是在计算机制图技术有了很大发展的今天，自动注记仍然没有得到很好解决。最近的研究表明，找到具有最佳效果的注记从时间上来讲是不可能的，因而自动注记算法都是希望在尽量短的时间内，得到尽可能好的注记效果。自动注记的主要难点在     

用于成像光谱数据特征矿物识别的人工神经网络结构研究

当某一问题很难甚至无法用数学方法建立精确模型时,人工神经网络的方法则显示了优势。对于一个具体问题,采用何种网络结构是至关重要的。本文以美国内华达州Cuprite矿区成像光谱数据特征矿物识别为例,采用6种不同结构的多层前馈网络模型,从其训练难度、运算效率及识别效果等方面进行了综合对比分析。     

A novel confidence estimation method for neural networks in multispectral image classification

Abstract

The Digital Earth concept has attracted much attention recently and this approach uses a variety of earth observation data from the global to the local scale. Imaging techniques have made much progress technically and the methods used for automatic extraction of geo-ralated information are of importance in Digital Earth science. One of these methods, artificial neural networks (ANN) techniques, have been effectively used in classification of remotely sensed images. Generally image classification with ANN has been producing higher or equal mapping accuracies than parametric methods. Comparative studies have, in fact, shown that there is no discernible difference in classification accuracies between neural and conventional statistical approaches. Only well designed and trained neural networks can present a better performance than the standard statistical approaches. There are, as yet, no widely recognised standard methods to implement an optimum network. From this point of view it might be beneficial to quantify ANN's reliability in classification problems. To measure the reliability of the neural network might be a way of developing to determine suitable network structures. To date, the problem of confidence estimation of ANN has not been studied in remote sensing studies. A statistical method for quantifying the reliability of a neural network that can be used in image classification is investigated in this paper. For this purpose the method is to be based on a binomial experimentation concept to establish confidence intervals. This novel method can also be used for the selection of an appropriate network structure for the classification of multispectral imagery. Although the main focus of the research is to estimate confidence in ANN, the approach might also be applicable and relevant to Digital Earth technologies.     

人工神经网络遥感影像分类模型及其与知识集成方法研究

以多层感知器（MLP）为例，探讨了地学知识与ANN融合进行遥感影像分类的方法。首先对MLP网络结构、学习算法及其改进进行分析；然后总结了MLP进行遥感影像分类的一般方法和存在的缺陷；发展了基于知识的MLP神经网络遥感影像分类模型，并具体利用基于规则的MLP方法进行了遥感土地覆盖分类的实验，把获得的结果与传统统计方法与一般ANN方法进行了综合比较，获得了有意义的结果。     

The Application of BP Networks to Land Suitability Evaluation

The back propagation (BP) model of artificial neural networks (ANN) has many good qualities comparing with ordinary methods in land suitability evaluation.Through analyzing ordinary methods‘ limitations,some sticking points of BP model used in land evaluation,such as network structure,learning algorithm,etc.,are discussed in detail,The land evaluation of Qionghai city is used as a case study.Fuzzy comprehensive assessment method was also employed in this evaluation for validating and comparing.     

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.     

基于神经网络趋势面分析的地价样点检验方法研究

建立了基于人工神经网络的地价趋势面分析模型,提出了基于该模型并结合可视化检视进行地价样点粗差检测的算法,实例验证了该方法的可行性.     

Artificial Neural Networks as a Method of Spatial Interpolation for Digital Elevation Models

This paper examines the performance of artificial neural networks (ANNs) as a method of spatial interpolation, when presented with irregular and regular samples of elevation data. The results of the ANN interpolation are compared with results obtained by kriging. Tests of spatial bias in the systematic errors contained in each of the neural network-derived DEMs were conducted using four attributes: slope, aspect, average direction and average distance from the nearest sampled value. Based on RMS and other evaluation measures, the accuracy of estimated DEMs from regular and irregular sample distributions using neural networks is lower than the accuracy level derived from kriging. The accuracy level of the ANN interpolators also decreases as the range of elevation values in DEMs increases. As reported in the literature, ANNs are approximate interpolators, and the pattern of under-prediction and over-prediction of elevation values in this study revealed that all estimated values fell within the range of sample elevations. Neural networks cannot predict values outside the range of elevation values contained in the sample, a property shared by other interpolators such as inverse weighted distance.     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Development of a modified neural network-based land cover classification system using automated data selector and multiresolution remotely sensed data

Integrating multiple images with artificial neural networks (ANN) improves classification accuracy. ANN performance is sensitive to training datasets. Complexity and errors compound when merging multiple data, pointing to needs for new techniques. Kohonen's self-organizing mapping (KSOM) neural network was adapted as an automated data selector (ADS) to replace manual training data processes. The multilayer perceptron (MLP) network was then trained using automatically extracted datasets and used for classification. Two hypotheses were tested: ADS adapted from the KSOM network provides adequate and reliable training datasets, improving MLP classification performance; and fusion of Landsat thematic mapper (TM) and SPOT images using the modified ANN approach increases accuracy. ADS adapted from the KSOM network improved training data quality and increased classification accuracy and efficiency. Fusion of compatible multiple data can improve performance if appropriate training datasets are collected. This proved to be a viable classification scheme particularly where acquiring sufficient and reliable training datasets is difficult.     

利用神经网络实现动态大系统稳定性分析与镇定

本文给出了利用人工神经网络（ＡＮＮ）理论和技术实现线性定常离散和连续时间动态大系统稳定性分析与镇定的方法（算法）；并在计算机上进行了一系列的数值实验，验证了该方法的正确性、有效性和可实现性。