An Iterative Interpolation Deconvolution Algorithm for Superresolution Land Cover Mapping

Abstract

Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms.