Road condition assessment by OBIA and feature selection techniques using very high-resolution WorldView-2 imagery

Accurate information on the conditions of road asphalt is necessary for economic development and transportation management. In this study, object-based image analysis (OBIA) rule-sets are proposed based on feature selection technique to extract road asphalt conditions (good and poor) using WorldView-2 (WV-2) satellite data. Different feature selection techniques, including support vector machine (SVM), random forest (RF) and chi-square (CHI) are evaluated to indicate the most effective algorithm to identify the best set of OBIA attributes (spatial, spectral, textural and colour). The chi-square algorithm outperformed SVM and RF techniques. The classification result based on CHI algorithm achieved an overall accuracy of 83.19% for the training image (first site). Furthermore, the proposed model was used to examine its performance in different areas; and it achieved accuracy levels of 83.44, 87.80 and 80.26% for the different selected areas. Therefore, the selected method can be potentially useful for detecting road conditions based on WV-2 images.     

遥感影像特征提取与选择及在影像分类中的应用

利用模式识别中特征提取和特征选择的相关理论对遥感影像的纹理特征进行遴选和变换处理,得到描述纹理的二次特征。实验证明这些新特征能够提高影像分类精度和分类效率。     

PS-DInSAR公共主影像的优化选取

在充分顾及DInSAR时空基线跨度及多普勒质心频率差异3个因素对干涉相关性影响的基础上,提出一种PS—DInSAR公共主影像的优化选取算法,采用综合相关测度作为总体干涉相关性的衡量指标,给出具体的综合相关函数模型及其求解方法。借助欧洲空间局（ESA）ERS-1／2卫星从1992—2002年间飞越上海地区所获取的25幅C波段SAR影像作为源数据,开展了PS—DInSAR公共主影像的优化选取实验。计算结果与分析表明,综合相关系数模型用于PS公共主影像的优化选取是有效的和可靠的。     

道路数据缩编更新的自动综合方法研究

缩编更新是当前GIS多尺度数据库更新的重要技术途径之一。针对我国国家1∶5万数据库更新工程的实际需求,研究分析了用较大比例尺新道路数据缩编更新较小比例尺道路数据的总体思路,提出了基于网眼密度的道路选取和顾及层次分析的道路匹配方法,实现了道路数据缩编更新的自动综合处理,并在1∶5万数据更新实践中进行了实验验证。     

GENTOOLS在国家1∶50000数据库更新工程中的应用研究

1∶50000地形数据库更新是一项复杂的地形数据库制作工程。传统的缩编技术工艺与相应的工具难以适应新的生产需要,适应于数字环境下的缩编生产工具的需求则变得非常迫切。GENTOOLS很好地解决了1∶50000数据库更新工程中的诸多难题,采用半自动和手动相结合的方法,大大提高了缩编效率,同时也为地形图缩编提供了一种新的选择。     

GIS科普资源发布系统实现的关键技术

科普资源发布系统利用GIS强大的空间查询与分析功能,方便市民浏览、查询与统计科普资源情况,掌握更多科普知识.本文主要论述了系统设计与实现过程中应用的关键技术,详细给出了栅格影像自动选择的方法,矢量栅格匹配的原理,以及统计图的绘制技术.最后介绍了系统的开发模式与软件环境,给出了统计效果图.     

地表下沉系数选取的LWPSO-BP方法研究

针对BP神经网络自身收敛速度慢、容易陷入局部极小点的缺点,本文以线性下降惯性权重粒子群优化算法(LLWPSO)为前处理器,优化BP网络的权值和阈值,利用实测资料数据,建立LWPSO-BP的地表下沉系数预计模型,并同普通BP模型预计结果对比结果表明:LWPSO-BP神经网络不仅训练速度快,而且预测精度明显提高,该模型对地...     

Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation

There is an urgent necessity to monitor changes in the natural surface features of earth. Compared to broadband multispectral data, hyperspectral data provides a better option with high spectral resolution. Classification of vegetation with the use of hyperspectral remote sensing generates a classical problem of high dimensional inputs. Complexity gets compounded as we move from airborne hyperspectral to Spaceborne technology. It is unclear how different classification algorithms will perform on a complex scene of tropical forests collected by spaceborne hyperspectral sensor. The present study was carried out to evaluate the performance of three different classifiers (Artificial Neural Network, Spectral Angle Mapper, Support Vector Machine) over highly diverse tropical forest vegetation utilizing hyperspectral (EO-1) data. Appropriate band selection was done by Stepwise Discriminant Analysis. The Stepwise Discriminant Analysis resulted in identifying 22 best bands to discriminate the eight identified tropical vegetation classes. Maximum numbers of bands came from SWIR region. ANN classifier gave highest OAA values of 81% with the help of 22 selected bands from SDA. The image classified with the help SVM showed OAA of 71%, whereas the SAM showed the lowest OAA of 66%. All the three classifiers were also tested to check their efficiency in classifying spectra coming from 165 processed bands. SVM showed highest OAA of 80%. Classified subset images coming from ANN (from 22 bands) and SVM (from 165 bands) are quite similar in showing the distribution of eight vegetation classes. Both the images appeared close to the actual distribution of vegetation seen in the study area. OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.     

图斑数据自动概括中面向地理特征的层结构选取

在信息定义和结构化选取的基础上,根据图斑的空间分布与距离联系,给出图斑数据地理要素层结构的定义和建立过程,从而实现图斑数据制图概括中对象选取的模型化。     

基于偏最小二乘回归的纹理特征线性组合

基于偏最小二乘回归技术对纹理特征进行线性组合,得到新的纹理特征来进行分类。实验表明,组合后的纹理特征不但提高了纹理分类的性能,而且具有一定的数据自适应能力。