文献类型：会议论文

英文题名：Moving window-based topographic normalization of optical satellite imagery for forest mapping in mountainous terrain

作者：Dengkui, Mo[1] Fuchs, Hans[1] Fehrmann, Lutz[1] Haijun, Yang[1] Kleinn, Christoph[1] Yuanchang, Lu[2]

第一作者：Dengkui, Mo

机构：[1] Department of Forest Inventory and Remote Sensing, Burckhardt Institute, Georg-August-Universit?t G?ttingen, Büsgenweg 5, 37077, Germany; [2] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China

会议日期：June 11, 2014 - June 14, 2014

会议地点：Changsha, China

语种：英文

外文关键词：Forestry - Landforms - Parameter estimation - Remote sensing

年份：2014

摘要：Relief has a significant impact on image classification in mountain areas because slope and aspect of the terrain together with the illumination geometry (solar zenith, solar azimuth angle and sensor position) make that one and the same land cover class has markedly different spectral signatures within one satellite image. Topographic normalization models help reduce intra-class spectral variability. This study proposes and evaluates a moving window-based rotation-correction topographic normalization model. We tested the algorithm with the latest Landsat 8 imagery in a region with very high forest cover in Shitai County, Anhui Province, China, which is characterized by a rough terrain with very steep slopes. Visual comparison and statistical analysis showed that the proposed method yielded better performance at a range of window sizes compared to uncorrected data or global correction methods. The heterogeneity of spectral signatures inside each land cover class could significantly be reduced, which may be partly due to the fact that a site-specific parameterization was used. Model performance was relatively stable over the tested range of window sizes. This new method for parameter estimation for topographic normalization is simple and straightforward, making this technique a suitable option for standard pre-processing of optical satellite imagery. ? 2014 IEEE.