地理研究 ›› 2001, Vol. 20 ›› Issue (5): 637-643.doi: 10.11821/yj2001050015

• 论文 • 上一篇    

遥感影像分类与地学知识发现的集成研究

王雷, 冯学智, 都金康   

  1. 南京大学城市与资源学系, 南京 210093
  • 收稿日期:2001-04-24 修回日期:2004-08-30 出版日期:2001-10-15 发布日期:2001-10-15
  • 作者简介:王雷(1977-),男,山东鱼台县人,南京大学2000级博士研究生。
  • 基金资助:

    中德合作”江宁土地利用与可持续发展”(SILUP)项目资助

On the integration between image classification and geographical knowledge discovery

WANG Lei, FENG Xue-zhi, DU Jin-kang   

  1. Department of Urban and Resources Science, Nanjing University, Nanjing 210093, China
  • Received:2001-04-24 Revised:2004-08-30 Online:2001-10-15 Published:2001-10-15

摘要:

遥感与地学之间存在着数据与知识上巨大的互补性。本文通过地面类型数据将遥感影像分类与地学知识发现结合起来:用遥感数据驱动发现地学知识,用地学知识解释、确认、检验遥感分类结果,并使用统计值和分布谱来定量化表达地学知识,形成一体化的遥感地学分类系统。

关键词: 影像分类, 地学知识发现, 分类精度评价

Abstract:

Great complementarity exists between remote sensing image data and geographical knowledge. This paper tries to unify the image classification and geographical knowledge discovery through ground classes data, i.e.,to discover geographical knowledge with remote sensing data drive, to confirm, explain and evaluate image classification result with geographical knowledge, and to represent geographical knowledge with statistic value and distribution atlas. All these come to be an incorporated Remote Sensing and Geographic Classification System. The steps of this method are as follows: firstly, to divide the image into relative big number(>20) of classes using the unsupervised classification; then overlay these unknown classes with the DEM data and get some statistic values and distribution atlas for each class; finally use these values and atlases to name,explain and evaluate each class of the classification result. Meanwhile the correlation between the ground object type and the topographical data is acquired and expressed as well. The example shows that this method makes the classification more efficient and reliable, and it is useful to express and discover the geographical knowledge. The conclusion is that, we can use other data to interpret the result of unsurpervised classification, name and check each class, and at the same time, acquire the geographical knowledge from the pattern in the image data.

Key words: image classification, geographical knowledge discovery, precision evaluation

  • TP79