Landsat-spot Digital Images Integration Using Geostatistical Cosimulation Techniques

Interpretation of remote sensing images into terrestrial attributes is very dependent of their spatial and spectral resolution. Normally, these types of resolution are contradictory: high spatial resolution sensors have a low spectral resolution whereas multispectral sensors have a low spatial resolution. Digital image-merging procedures are techniques that aim at integrating the multispectral characteristics in a high spatial resolution image. The main objective is to obtain synthetic images that combine the advantages of the high spatial resolution and high spectral resolution of both types of images. Unfortunately, the most commonly used methods can not be considered real merging methods. They consist in a simple substitution of the high-spectral images with a high-spatial resolution image based on the correlation between both data sets. The images obtained by those merging/substitution procedures, although honouring the values of multispectral images, do not account for the spatial patterns of high spatial resolution images. In this paper a new merging approach is presented. The method is based on a geostatistical technique of direct sequential cosimulation that aims at producing images with the spatial patterns of high spatial resolution images and the local values of the coarse multispectral images. The method was applied to Landsat-TM and SPOT-P images and the results were compared with the images provided by other common merging procedures. Using the proposed geostatistical procedure, the merged images preserve the spectral characteristics of the higher-spectral resolution images in terms of both descriptive statistics and band correlation coefficients.