• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Asian conference on remote sensingACRS >A Support Vector Machine Approach on Object Based Image Analysis for Feature Extraction from High Resolution Images
【24h】

A Support Vector Machine Approach on Object Based Image Analysis for Feature Extraction from High Resolution Images

机译：高分辨率图像特征提取的基于对象图像分析的支持向量机方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Satellite images are the most important available data sources for generation and updating of available maps. They have highly improved in terms of spatial, spectral and temporal resolutions and by the sheer volume of collected images, the necessity of simplification of automation in feature extraction. Road data play a key role in urban planning, traffic management, military applications, and vehicle navigation as well as for decision making in numerous applications. The faster updation of road infrastructure is a need because the technology has brought map in the hands of people in the form of mobile phones and tablets. Road detection is one of the major issues of the road infrastructure extraction. Its accuracy depends on the type of methodology used. An attempt is made here to analyse the first order, the co-occurrence texture features and image transforms useful for discriminating roads from other features specially the buildings. The identified dataset forms high dimension feature space and the Support Vector Machine is a theoretically superior machine learning methodology with great results in classification of high dimensional datasets. In the past, SVMs have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based techniques towards object-based representation, the dimensions of remote sensing imagery feature space increases significantly. An SVM approach for classification was followed, based on primitive image objects produces by a multi-resolution segmentation algorithm. The SVM procedure produced the final object classification results which were compared to the Nearest Neighbor classifier results and were found to give better results in OBIA domain.
机译：卫星图像是最重要的可用数据源，用于发电和更新可用地图。它们在空间，光谱和时间分辨率方面具有高度改善，并且通过收集图像的纯粹体积，在特征提取中简化自动化的必要性。道路数据在城市规划，交通管理，军事应用和车辆导航中发挥着关键作用，以及在许多应用中的决策。道路基础设施更快的更新是一种需要，因为该技术在手机和平​​板电脑的形式下载了人们手中的地图。道路检测是道路基础设施提取的主要问题之一。其准确性取决于所用方法的类型。此后尝试分析第一顺序，其共同发生纹理特征和图像变换对于鉴别来自其他特征的公路，特别是建筑物。所识别的数据集形成高尺寸特征空间，支持向量机是理论上优越的机器学习方法，在高维数据集的分类中具有很大的结果。在过去，SVM已经测试并仅作为基于像素的图像分类器进行评估。从基于像素的技术转向基于对象的表示，遥感图像特征空间的尺寸显着增加。基于原始图像对象通过多分辨率分割算法产生的基于原始图像对象，遵循SVM方法。 SVM程序产生了与最近的邻邻分类器结果进行比较的最终对象分类结果，并发现在OBIA域中提供更好的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号