Pattern decomposition method for hyper-multi-spectral data analysis

Abstract

The ‘pattern decomposition method’ (PDM) is a new analysis method originally developed for Landsat Thematic Mapper (TM) satellite data. Applying the PDM to the radiospectrometer data of ground objects, 121 dimensional data in the wavelength region 350–2500 nm were successfully reduced into three-dimensional data. The nearly continuous spectral reflectance of land cover objects could be decomposed by three standard spectral patterns with an accuracy of 4.17% per freedom. We introduced a concept of supplementary spectral patterns for the study of specific ground objects. As an example, availability of a supplementary spectral pattern that can rectify standard spectral pattern of vivid vegetation for spectra of withered vegetation was studied. The new Revised Vegetation Index based on Pattern Decomposition (RVIPD) for hyper-multi-spectra is proposed as a simple function of the pattern decomposition coefficients including the supplementary vegetation pattern. It was confirmed that RVIPD is linear to the area cover ratio and also to the vegetation quantum efficiency.

Acknowledgments

This work was supported under the ADEOS-II/GLI project by the National Space Development Agency of Japan (NASDA) and the Academic Frontier Promotion project by the Ministry of Education, Science, Sports and Culture, Japan.

Notes

^†Present address: Center for Environmental Remoto Sensing, China University, China 263-8522, Japan

^‡Deceased.

Additional information

Notes on contributors

A. Ono†

†Present address: Center for Environmental Remoto Sensing, China University, China 263-8522, Japan

R. Urabe‡

‡Deceased.