Super-resolution land cover pattern prediction using a Hopfield neural network

Abstract

Landscape pattern represents a key variable in management and understanding of the environment, as well as driving many environmental models. Remote sensing can be used to provide information on the spatial pattern of land cover features, but analysis and classification of such imagery suffers from the problem of class mixing within pixels. Soft classification techniques can estimate the class composition of image pixels. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field-of-view (IFOV) represented by the pixel. Techniques to provide an improved spatial representation of land cover targets larger than the size of a pixel have been developed. However, the mapping of subpixel scale land cover features has yet to be investigated. We recently described the application of a Hopfield neural network technique to super-resolution mapping of land cover features larger than a pixel, using information of pixel composition determined from soft classification, and now show how our approach can be extended in a new way to predict the spatial pattern of subpixel scale features. The network converges to a minimum of an energy function defined as a goal and several constraints. Prior information on the typical spatial arrangement of the particular land cover types is incorporated into the energy function as a semivariance constraint. This produces a prediction of the spatial pattern of the land cover in question, at the subpixel scale. The technique is applied to synthetic and simulated Landsat Thematic Mapper (TM) imagery, and compared to results of an existing super-resolution target identification technique. Results show that the new approach represents a simple, robust, and efficient tool for super-resolution land cover pattern prediction from remotely sensed imagery.