Main Article Content
Most target detection algorithms suffer from the limitation that they can detect only the full pixels of the target while the target may also reside, besides the full pixel, partially in several surrounding pixels. In some cases, the target may even be embedded completely within the pixel. Both these cases are known as subpixel target detection problem. Many target detection applications, however, require detection of full pixels as well as detection of subpixel targets in the surrounding pixels which constitute a case of the mixed pixel. The problem is addressed by full pixel detection followed by spectral unmixing to determine the abundance fraction of the target. Though spectral unmixing gives the abundance fractions, it still does not give the spatial distribution/ arrangement of subpixels of the target with the surrounding pixels. The process of optimizing the spatial distribution of subpixels inside any given pixel based on the available abundance fractions is known as super resolution. This paper investigates Inverse Euclidean distance based super resolution. The algorithm performs well at different scale factors both for synthetic and real hyperspectral data which can aid the super resolution process significantly and thereby enhance the identification and recognition of target. A comparative assessment is also performed with Pixel Swap algorithm.