PAN-SHARPENING BASED ON JOINT SALIENCY DETECTION FOR MULTIPLE REMOTE SENSING IMAGES

Requirements of spectral and spatial quality differ from region to region in remote sensing images, which is a significant challenge for pan-sharpening. Joint saliency analysis not only fulfills these demands, but also ensures the consistency by considering the mutual information of multiple images. Thus, we propose a pan-sharpening method based on joint saliency analysis and improved intensity–hue–saturation (IHS) for multiple remote sensing images. Firstly, we introduce an improved IHS method to obtain an accurate estimation of the intensity component. Then, we design a joint saliency analysis method based on global contrast calculation and intensity feature extraction, which is subsequently compensated by texture features to generate adaptive injection gains. Finally, we use the injection gains to inject the detail into the multispectral (MS) image. Experimental results demonstrate that our method has better performance in guaranteeing consistency in multiple images, improving spatial quality and preserving spectral fidelity.