• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>ISPRS International Journal of Geo-Information >Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge
【24h】

Study on Multi-Scale Window Determination for GLCM Texture Description in High-Resolution Remote Sensing Image Geo-Analysis Supported by GIS and Domain Knowledge

机译：GIS和领域知识支持的高分辨率遥感影像地理分析中多尺度窗确定GLCM纹理描述的研究

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

摘要

Texture features based on the gray-level co-occurrence matrix (GLCM) can effectively improve classification accuracy in geographical analyses of optical remote sensing (RS) images, with the parameters of scale of the GLCM texture window greatly affecting the validity. By analyzing human visual attention characteristics for geo-texture cognition, it was found that there is a strong correlation between the texture scale parameters and the domain shape knowledge in a specified geo-scene. Therefore, a new approach for quickly determining the multi-scale parameters of the texture with the assistance of a geographic information system (GIS) and domain knowledge is proposed in this paper. First, the validity of domain knowledge from an existing GIS database is measured by spatial data mining algorithms, including spatial partitioning, image segmentation, and space-time system evaluation. Second, the general domain shape knowledge of each category is described by the GIS minimum enclosing rectangle indices and rectangular-degree indices. Then, the corresponding multi-scale texture windows can be quickly determined for each category by a correlation analysis with the shape indices. Finally, the Fisher function is used to evaluate the validity of the scale parameters. The experimental results show that the multi-scale value keeps a one-to-one relationship with the classified objects, and their value ranges are from a few to tens, instead of the smaller values of a traditional analysis; thus, effective texture features at such a scale can be built to identify categories in a geo-scene. In this way, the proposed method can increase the total number of categories for a certain geo-scene and reduce the classification uncertainty, as well as better meet the requirements of large-scale image geo-analysis. It also has as high a calculation efficiency and as good a performance as the traditional enumeration method.
机译：基于灰度共生矩阵（GLCM）的纹理特征可以有效提高光学遥感（RS）图像地理分析中的分类精度，并且GLCM纹理窗口的比例尺参数极大地影响了有效性。通过分析人的视觉注意特征对地理纹理的认知，发现在特定的地理场景中，纹理比例参数与域形状知识之间存在很强的相关性。因此，本文提出了一种借助地理信息系统（GIS）和领域知识快速确定纹理的多尺度参数的新方法。首先，通过空间数据挖掘算法（包括空间分区，图像分割和时空系统评估）来评估现有GIS数据库中领域知识的有效性。其次，每个类别的一般领域形状知识由GIS最小包围矩形索引和矩形度索引描述。然后，可以通过与形状指标的相关性分析针对每个类别快速确定对应的多尺度纹理窗口。最后，使用Fisher函数来评估尺度参数的有效性。实验结果表明，多尺度值与被分类对象保持一一对应的关系，其取值范围从几到几十，而不是传统分析的较小值。因此，可以建立这种规模的有效纹理特征，以识别地质场景中的类别。这样，所提出的方法可以增加一定地理场景的类别总数，减少分类的不确定性，更好地满足大规模图像地理分析的要求。它也具有与传统枚举方法一样高的计算效率和性能。

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