Baig, MHA等:Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance
来源:发布时间:2014-06-13
Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance
作者:Baig, MHA (Baig, Muhammad Hasan Ali)[ 1,2 ] ; Zhang, LF (Zhang, Lifu)[ 1 ] ; Shuai, T (Shuai, Tong)[ 1,2 ] ; Tong, QX (Tong, Qingxi)[ 1 ]
REMOTE SENSING LETTERS
卷: 5 期: 5 页: 423-431
DOI: 10.1080/2150704X.2014.915434
出版年: MAY 4 2014
摘要
The tasselled cap transformation (TCT) is a useful tool for compressing spectral data into a few bands associated with physical scene characteristics with minimal information loss. TCT was originally evolved from the Landsat multi-spectral scanner (MSS) launched in 1972 and is widely adapted to modern sensors. In this study, we derived the TCT coefficients for the newly launched (2013) operational land imager (OLI) sensor on-board Landsat 8 for at-satellite reflectance. A newly developed standardized mechanism was used to transform the principal component analysis (PCA)-based rotated axes through Procrustes rotation (PR) conformation according to the Landsat thematic mapper (TM)-based tasselled cap space. Firstly, OLI data were transformed into TM TCT space directly and considered as a dummy target. Then, PCA was applied on the original scene. Finally, PR was applied to get the transformation results in the best conformation to the target image. New coefficients were analysed in detail to confirm Landsat 8-based TCT as a continuity of the original tasselled cap idea. Results show that newly derived set of coefficients for Landsat OLI is in continuation of its predecessors and hence provide data continuity through TCT since 1972 for remote sensing of surface features such as vegetation, albedo and water. The newly derived TCT for OLI will also be very useful for studying biomass estimation and primary production for future studies.
通讯作者地址: Zhang, LF (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Beijing, Peoples R China
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