...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IAES International Journal of Artificial Intelligence >Support Vector Machines for Object Based Building Extraction in Suburban Area using Very High Resolution Satellite Images, a Case Study: Tetuan, Morocco
【24h】

Support Vector Machines for Object Based Building Extraction in Suburban Area using Very High Resolution Satellite Images, a Case Study: Tetuan, Morocco

机译：支持向量机，用于使用超高分辨率卫星图像在郊区进行基于对象的建筑物提取，案例研究：摩洛哥得土安

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

摘要

Many fields of artificial intelligence have been developed such as computational intelligence and machine learning involving neural networks, fuzzy systems, genetic algorithms, intelligent agents and Support Vector Machines (SVM). SVM is a machine learning methodology with great results in image classification. In this paper, we present the potential of SVMs to automatically extract buildings in suburban area using Very High Resolution Satellite (VHRS) images. To achieve this goal, we use object based approach: Segmentation before classification in order to create meaningful image objects using color features. In the first step, we form objects with the aid of mean shift clustering algorithm. Then, SVM classifier was used to extract buildings. The proposed method has been applied on a suburban area in Tetuan city (Morocco) and 83.76% of existing buildings have been extracted by only using color features. This result can be improved by adding other features (e.g., spectral, texture, morphology and context).DOI: http://dx.doi.org/10.11591/ij-ai.v2i1.1781.
机译：人工智能的许多领域已经得到发展，例如涉及神经网络，模糊系统，遗传算法，智能代理和支持向量机（SVM）的计算智能和机器学习。 SVM是一种机器学习方法，在图像分类中取得了不错的成绩。在本文中，我们介绍了SVM使用超高分辨率卫星（VHRS）图像自动提取郊区建筑物的潜力。为了实现此目标，我们使用基于对象的方法：分类之前的分割，以便使用颜色特征创建有意义的图像对象。第一步，我们借助均值漂移聚类算法形成对象。然后，使用SVM分类器提取建筑物。该方法已在摩洛哥得土安市的郊区应用，仅使用颜色特征就提取了83.76％的现有建筑物。可以通过添加其他功能（例如光谱，纹理，形态和上下文）来改善此结果.DOI：http://dx.doi.org/10.11591/ij-ai.v2i1.1781。

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