Hyper-Sharpening Based on Spectral Modulation

Abstract

Hyperspectral (HS) image sharpening (namely hyper sharpening) with an auxiliary sensor, such as multispectral (MS) or panchromatic sensor, has attracted a great deal of attention for the past decade. A number of hyper-sharpening techniques, aiming at enhancing the spatial resolution of HS images, have been developed and demonstrated their effectiveness especially on synthetic or simulated data. Nevertheless, since the differences between different remote sensing systems or imaging conditions are complicate, it results in a serious spectral distortion when applying on real remote sensing data acquired by different sensors under different acquisition times or conditions. Unfortunately, very few works have considered this issue. In this paper, a new hyper-sharpening framework based on spectral modulation is proposed to better preserve spectral information when fusing with MS data acquired by a different sensor. The goal of this paper is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Two approaches, originating in MS pan-sharpening field, are introduced as examples under this framework, namely high-pass details injection model and band-dependent spatial-detail model. Experiments on three HS and MS datasets acquired by different platforms demonstrate that the proposed framework is beneficial to the spectral fidelity of the fused image compared with some state-of-the-art hyper-sharpening techniques. Meanwhile, it is also easy to implement and has a certain advantage in enhancing the spatial details.