A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images

Abstract

A new subpixel mapping (SPM) method based on a super-resolution convolutional neural network (SRCNN) is proposed to generate subpixel land cover maps for hyperspectral images. The SRCNN is used to restore the image spatial resolution from a coarse input image, which is equivalent to interpolation. First, an efficient subpixel convolutional neural network, which is a state-of-the-art SRCNN, is utilized to calculate the subpixel soft class value via a transfer learning strategy. Then, a classifier is used to transform the subpixel soft class values to hard-classified land cover maps with the constraint of fraction images. Experiments on three different hyperspectral images demonstrate that the SPM accuracy of the proposed SRCNN-based method is significantly better than those of three traditional SPM methods. In addition, the SRCNN-based SPM method has a simplified calculation process, does not require training data, and is less time consuming. This article provides a new solution for SPM of hyperspectral images.