Inversion of deep networks for modelling variations in spatial distributions of land cover classes across scales

In this paper, we propose the use of network inversion for modeling the variation of class distributions with scale. Unlike the state of the art methods that predict the mapping between coarser and finer scale patches without considering the distributions at coarser scale, our approach uses coarser scale features for effective reconstruction. This is the pioneer work of using network inversion for the purpose. Analysis over the proposed framework reveals that both the computational performance and accuracy varies with the depth of the network as well as the size and number of filters in each layer. Also the performance of the approach has been found to improve with the increase in the number of input feature maps. Investigations over standard datasets indicate that the proposed approach performs much better than the recent sub-pixel classification as well as super resolution techniques.