Super-resolution analysis for accurate mapping of land cover and land cover pattern

This paper presents a super-resolution mapping technique as a means to gain accurate information of land cover, and especially its spatial pattern, at a sub-pixel scale. This technique extends the application of an established Hopfield neural network of super-resolution mapping technique by providing its input with a fusion of a time series coarse spatial but fine temporal resolution images. To illustrate this technique, a series of daily MODIS 250m images was acquired and fused. Using a Landsat ETM+ 30m image as ground data, results demonstrated that a Hopfield network that uses time series information produces significantly more accurate representation of land cover mapping in terms of thematic accuracy and spatial pattern prediction than by using a single image into Hopfield network or into hard classification techniques.