首页    期刊浏览 2022年07月07日 星期四
登录注册

文章基本信息

  • 标题:DTM generation in forest regions from satellite stereo imagery
  • 本地全文:下载
  • 作者:J. Tian ; T. Krauss ; P. Reinartz
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2014
  • 卷号:XL-1
  • 页码:401-405
  • DOI:10.5194/isprsarchives-XL-1-401-2014
  • 出版社:Copernicus Publications
  • 摘要:Satellite stereo imagery is becoming a popular data source for derivation of height information. Many new Digital Surface Model (DSM) generation and evaluation methods have been proposed based on these data. A novel Digital Terrain Model (DTM) extraction method based on the DSM from satellite stereo imagery is proposed in this paper. Instead of directly filtering the DSM, firstly a single channel based classification method is proposed. In this step, no multi-spectral information is used, because for some stereo sensors, like Cartosat-1, only panchromatic channels are available. The proposed classification method adopts the random forests method to get initial probability maps of the four main classes in forest regions (high-forest, low-forest, ground, and buildings). To cover the pepper and salt effect of this pixel based classification method, the probability maps are further filtered based on the adaptive Wiener filtering. Then a cube-based greedy strategy is applied in generating the final classification map from these refined probability maps. Secondly, the height distances between neighboring regions are calculated along the boundary regions. These height distances can be used to estimate the relative region heights. Thirdly, the DTM is extracted by subtracting these relative region heights from the DSM in the order of: buildings – low forest – high forest. In the end, the extracted DTM is further smoothed using median filter
  • 关键词:Optical Stereo Data; DTM; DSM; Forest; Random Forests
国家哲学社会科学文献中心版权所有