Method for extremum type multi-criterial estimates for fusion of images of different on-board remote sensing imaging systems

The paper is devoted to the suggested method for forming of extremum type multi-criterial estimates for fusion of images of different on-board remote sensing imaging systems. It is well-known that fusion of multispectral images with panchromatic images with high geometric resolution makes it possible to compensate the natural decrease of spectral resolution upon increasing the geometric resolution. However, as it has been proved by the held analysis of the mentioned methods, when performing the fusion of two images, the parametric interrelation between two such major parameters as autocorrelation and mean standard deviation of intensity is not taken into account where the spatial resolution is functions as a parameter. The article is devoted to the analysis of feasibility of developing new methods for the fusion of images with different spatial resolution formed by on-board scanner type imaging systems. It is noted that these imaging systems are included into complex of measuring instruments installed on board of a carrier, for example, a low engine airplane, a helicopter or an unmanned aerial vehicle. The task on optimum fusion of two images of different on-board imaging systems with different geometrical resolution taking into consideration of three possible filtration methods is suggested. The method for evaluation of images on value of scalar convolution of major statistic parameters of images by condition of reaching extremum by value of calculated optimum geometric resolution upon which the minimum difference between calculated and initial values are provided is developed. In this case, the initial image should be processed by relevant filtration method. The alternative variant for implementation of the suggested method is described. It is noted that the use of combined estimates of initial images opens new possibilities in sphere of fusion of images with different geometric resolutions.

multi-criterial estimates, images, geometrical resolution, ltration, optimization, images fusion

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