Radiometric quality improvement of hyperspectral remote sensing images: a technical tutorial on variational framework

Abstract

In hyperspectral remote sensing imagery, the sensor, atmosphere, topography, and other factors often bring about some degradations, such as noise, haze, clouding, and shadowing. Due to inevitable tradeoff between spatial resolution and spectral resolution, low spatial details of hyperspectral images (HSIs) also limit the range of potential applications. Compensating for these degradations through quality improvement is a key preprocessing step in the exploitation of HSIs. A comprehensive analysis of the quality improvement techniques for HSIs is presented. The closely connected techniques, such as denoising, destriping, dehazing, cloud removal, and super-resolution, are linked as a whole by a general reconstruction model in a variational framework. Furthermore, we classify the methods into four categories according to their processing strategies for HSIs, including single-channel prior-based model, cross-channel prior-based model, tensor-based model, and data-driven prior-based model. Then, for several specific tasks, we briefly introduce their architectures of quality improvement, which combine different models and available complementary information from other spectral bands and/or temporal/sensor images. Some experimental results in different tasks are presented to show the effect of variational framework and draw some meaningful conclusions. Finally, some advantages on variational framework are discussed, and several promising directions are provided to serve as guidelines for future work.