28 October 2006 Narrowband vegetation index performance using the AVIRIS hyperspectral remotely sensed data
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Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64190M (2006) https://doi.org/10.1117/12.712912
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China
Abstract
The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis, standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a pixel's reflectance. The later explanation is much more comprehensible than the former for the reason of additional supplementary pattern presence when necessary. The vegetation index based on the UPDM (VIUPD) is expressed as a linear sum of the pattern decomposition coefficients. Here, the VIUPD was used to examine vegetation amounts and degree of terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index (NDVI), and an enhanced vegetation index (EVI). This paper described the calculation of VIUPD, using AVIRIS airborne remotely sensed data. The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than the NDVI and EVI.
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Lifu Zhang, Lifu Zhang, Lei Yan, Lei Yan, Shaowen Yang, Shaowen Yang, } "Narrowband vegetation index performance using the AVIRIS hyperspectral remotely sensed data", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190M (28 October 2006); doi: 10.1117/12.712912; https://doi.org/10.1117/12.712912
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