Sub pixel level arrangement of spatial dependences to improve classification accuracy

The colors in universe have sharp boundaries everybody is aware of specifically wherever a color starts and wherever it ends and that any color communicates the details about the targets in the scene in a much better way and that this detailed information can be used to further polish the interpretation of an imaging system. In this paper, the proposed subpixel level arrangements of spatial dependences provide super resolved landuse landcover information using the output of soft classified fractional values. The output of soft classifier satisfies the constraint of non-negativity and sum to 1 instead of whatever their “natural” total is of fractional abundance within the pixels. This phenomenon is also discussed while defining mixed pixels, the pixels at boundary contain both the colors in a proportion so that the pixel appears the color different from either of two. This paper main goal is to extract the information from mixed pixels and subpixel analysis with the subpixel level arrangements of spatial dependences to get the super resolved information.