A Super-Resolution Mapping Using a Convolutional Neural Network
Teerasit Kasetkasem · 2019
In this paper, we propose an approach for super-resolution land cover mapping on remote sensing images based on a Convolutional Neural Network (CNN). Here, the CNN is trained to match the input subimages to the super resolution map around the training pixels. Since there are so many possible configurations of super-resolution map on a given set of pixels, a large number of training samples are required. To reduce the number of training samples, we converted the super-resolution to a set of level set functions and used the minimum mean square error between the predicted and actual level set functions as the training objective. The QUICKBIRD satellite image data cover a part of Kasetsart University's Bangkhen campus was used for evaluation. Experimental results showed that the proposed method has achieved superior accuracy than both Hopfield and Pixel-Swapping methods.