SUB-PIXEL MAPPING WITH HYPERSPECTRAL IMAGES USING SUPER-RESOLUTION

Hyperspectral images are rich in spectral content but their spatial resolution is relatively poor. It can lead to mixed pixels and sub-pixel targets. In order to improve the reliability of information provided by hyperspectral image analysis and make the results practically usable, one needs to improve their spatial resolution. Due to physical constraints and associated cost, increasing the resolution by improving the sensors may not be a practical option. Thus one effective solution is some form of post-processing of hyperspectral data. Such an algorithmic resolution enhancement is called "superresolution". In this paper single image super-resolution of hyperspectral image has been attempted. The use of Hopfield Neural Network for successful landuse/landcover classification of Hyperspectral image has been shown. A successful attempt was made to improve initialization of the Hopfield neural network. The results were verified visually as well as statistically.