Sensor‐independent analysis method for hyperspectral data based on the pattern decomposition method

Abstract

This paper describes a modified pattern decomposition method with a supplementary pattern. The proposed approach can be regarded either as a type of spectral mixing analysis or as a kind of multivariate analysis; the later explanation is more suitable considering the presence of the additional supplementary patterns. The sensor‐independent method developed herein uses the same normalized spectral patterns for any sensor: fixed multi‐band (1260 bands) spectra serve as the universal standard spectral patterns. The resulting pattern decomposition coefficients showed sensor independence. That is, regardless of sensor, the three coefficients had nearly the same values for the same samples. The estimation errors for pattern decomposition coefficients depended on the sensor used. The estimation errors for Landsat/MSS and ALOS/AVNIR‐2 were larger than those of Landsat/TM (ETM+), Terra/MODIS and ADEOS‐II/GLI. The latter three sensors had negligibly small errors.

Acknowledgments

The authors thank A. Ono of Otemae University for encouraging us to write this paper. This work was supported under the ADEOS‐II/GLI Project of the Japan Aerospace Exploration Agency (JAXA), the Academic Frontier Promotion Project of the Ministry of Education, Science, Sports and Culture of Japan, and the 973 Project of the People's Republic of China (project no. 2003CB415205).