The chamacteristic polynomiai of a positive reciprecal matrix has sorne notewerthy properties. They are deeply related to the notion of consistency ofa pairwise comparison rnatrix of AHP. Based oll the results, we propose a method ± 'or estimating a rnissing entry of an incomplete pairwise comparison matrix.
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.
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