文献类型：期刊文献

英文题名：Local Parameter Estimation of Topographic Normalization for Forest Type Classification

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

第一作者：Mo, Dengkui

通信作者：Mo, DK[1]

机构：[1]Univ Gottingen, Fac Forest Sci & Forest Ecol, Burckhardt Inst, Chair Forest Inventory & Remote Sensing, D-37077 Gottingen, Germany;[2]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

年份：2015

卷号：12

期号：9

起止页码：1998-2002

外文期刊名：IEEE GEOSCIENCE AND REMOTE SENSING LETTERS

收录：;EI(收录号：20153301176648);Scopus(收录号：2-s2.0-85027921362);WOS:【SCI-EXPANDED(收录号:WOS:000359579000040)】；

基金：This work was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Lin4Carbon Project under Grant 033L049C, by the Chinese Academy of Forestry under Grant Lin2Value-CAFYBB2012013, and by the National Natural Science Foundation of China under Grant 31470643 and Grant 31100412.

语种：英文

外文关键词：Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM); empirical parameter estimation; Landsat 8; local window; topographic normalization

摘要：Radiometric distortions caused by rugged terrain make the classification of forest types from satellite imagery a challenge. Various band-specific topographic normalization models are expected to eliminate or reduce these effects. The quality of these models also depends on the approach to estimate empirical parameters. Generally, a global estimation of these parameters from a whole satellite image is simple, but it may tend to overcorrection, particularly for larger areas. A land-cover-specific method usually performs better, but it requires obtaining a priori land classification, which presents another challenge in many cases. Empirical parameters can be directly estimated from local pixels in a given window. In this letter, we propose and evaluate a central-pixel-based parameter estimation method for topographic normalization using local window pixels. We tested the method with Landsat 8 imagery and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) in very rough terrain with diverse forest types. Visual comparison and statistical analyses showed that the proposed method performed better at a range of window sizes compared with an uncorrected image or with a global parameter estimation approach. The intraclass spectral variability of each forest type has been reduced significantly, and it can yield higher accuracy of forest type classification. The proposed method does not require the a priori knowledge of land covers. Its simplicity and robustness suggest that this method has the potential to be a standard preprocessing approach for optical satellite imagery, particularly for rough terrain.