Super-resolution mapping using multiple observations and Hopfield neural network

Super-resolution mapping is used to produces thematic maps at a scale finer than the source images. This paper presents a new super-resolution mapping approach that exploits the typically fine temporal resolution of coarse spatial resolution images as it input and an adoption of an active threshold surface using Hopfield neural network as a means to map land cover at a sub-pixel scale. The results demonstrated that the proposed technique is slightly more accurate than the existence technique in terms of site specific accuracy and produce better visualization on individual land cover map.