Enhancing the Spatial Resolution of Hyperspectral Images Combining High-Accuracy Surface Modeling and Subpixel Unmixing

Abstract

Hyperspectral sensors can rapidly acquire high-quality spectral data, very useful for urban monitoring applications. Unfortunately, their spatial detail is not fine enough, and methods to enhance this resolution are required. However, conventional super-resolution (SR) methods for multispectral data do not match the requirements needed to maintain high spectral fidelity. Therefore, this article proposes a hyperspectral SR method that combines subpixel mapping and interpolation, and whose main aim is to enhance urban monitoring. This method aims to guarantee spectral quality and minimize computational time through a high-precision surface interpolation method based on curve theory. Moreover, unmixing-based subpixel mapping is exploited to introduce unmixing information. Finally, using wavelet transforms, the method integrates the effective information from the two previous approaches, obtaining urban hyperspectral images with enhanced spatial details and spectral fidelity. This method has been subjected to a comprehensive experimentation, affirming that the proposed method surpasses the current state-of-the-art SR results in terms of performance and effectiveness.