[Comparison among remotely sensed image fusion methods based on spectral response function].

Remotely sensed image fusion is a critical issue, and many methods have been developed to inject features from a high spatial resolution panchromatic sensor into low spatial resolution multi-spectral images, trying to preserve spectral signatures while improving spatial resolution of multi-spectral images. However, no explicit physical information of the detection system has been taken into account in usual methods, which might lead to undesirable effects such as severe spectral distortion. Benefiting from the proper decomposition of the image fusion problem by a concise image fusion mathematical model, the present paper focuses on comparing reasonable modulation coefficient of spatial details based on analysis of the spectral response function (SRF). According to the classification of former methods, three modulation coefficients based on SRF of sensors were concluded, which lead to three image fusion methods incorporating spatial detail retrieved by Gaussian high-pass filter. All these methods were validated on Ikonos data compared to GS and HPM method.