Fusion of satellite imagery with different spatial resolutions based on statistical techniques

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The Landsat TM sensor for thermal infrared measurement has lower spatial resolution than that of other six measurements. Nishii et al (1996) proposed a procedure to correct the infrared measurement by a linear combination of remaining high-resolution measurements. However, the linear combination based on all measurements does not always give a better result. For the selection problem, Nishii, Kusanobu and Nakaoka (1999) take an information theoretic approach (AIC) by Akaike (1973). AIC in our case is reduced to that in canonical correlation analysis which is obtained by Fujikoshi (1985). An averaged AIC is proposed for the problem and the procedure shows a good performance for actual TM images.Our other results related to the satellite data analysis are as follows :- It is explained that the kappa statistic, which is widely used for assessing the accuracy of land-cover classification, is not suitable for the accuracy assessment. The criterion based on Kullback-Leibler divergence is alternately proposed. (Nishii and Tanaka, 1999)- The regression model between forest and population is extended to the spatial nonlinear regression with relief as a new covariate. The new model shows a good fit for an actual data of Hiroshima.(Tanaka and Nishui, 1997)- It is shown that the lung cancer mortality in local governments is significantly related to the distance from the shoreline through spatial nonlinear regression. This suggests that the further epidemiological study is required. (in preparation)- For the denoising purpose, time series data is expanded by the wavelet functions and Bayes model is used for shrinking the wavelet coefficients. The procedure is shown to denoise successfully various numerical examples. (in preparation)