Learning-Based Superresolution Land Cover Mapping

Abstract—Abstract Superresolution mapping (SRM) is a technique for
generating a fine-spatial-resolution land cover map from coarsespatial-resolution fraction images estimated by soft classification.The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose priormodel is learned fromother available fine-spatial-resolution land cover maps, is proposed. The approach is based on the assumption that the spatial arrangement of the land cover components for mixed pixel patches with similar fractions is often similar. The proposed SRM algorithm produces a learning database that includes a large number of patch pairs for which there is a fine- and coarse-spatial-resolution representation for the same area. From the learning database, patch pairs that have similar coarse-spatial-resolution patches as those in the input fraction images are selected. Fine-spatial-resolution patches in these selected patch pairs are then used to estimate the latent finespatial-resolution land cover map by solving an optimization problem. The approach is illustrated by comparison against state-of-the-art SRMmethods using land cover map subsets generated from the USA’s National Land Cover Database. Results show that the proposed SRM algorithm better maintains the spatial pattern of land covers for a range of different landscapes. The proposed SRM algorithm has the highest overall accuracy and kappa values in all of these SRM algorithms, by using the entire maps in the accuracy assessment. Index Terms—Learning database, neighboring patches, patch pairs, superresolution mapping (SRM).