Fusion+: A Multi-Sensor Image Fusion for Very High-Resolution Satellite Imagery

A very detailed image, such as very high resolution satellite imagery, is necessary for many applications. By enhancing the image’s detail and reducing its color distortion, we can optimize its use. Image fusion is a method to achieve this goal. This paper proposes a novel method of multi-sensor image fusion called Fusion+. This method enhances spatial resolution, sharpens the image using the kernel of image enhancement, and preserves radiometric consistency using the histogram specification. In addition, the Fusion+ has a shift adjustment between the input images to address the geometric issue, especially for images from multiple sensors. The proposed method can be used in its entirety for both single-sensor and multi-sensor image fusion. The fused image obtained by the proposed method also increased the image’s detail and was sharpest compared to the other methods. The universal image quality index, peak signal-to-noise ratio, and structural similarity index measure are also sufficient to maintain the radiometric consistency of the original image. The results also showed that the fused image had maximum detail increase, minimum color distortion, and a natural color appearance. Fusion+ obtained an average SSIM score of 0.779, an average PSNR of 29.342, and an average UQI of 0.973 on single-sensor fused images, as well as SSIM averages of 0.556, an average PSNR of 27.896, and an average UQI of 0.947 on multi-sensor fused images. The Fusion+ method demonstrates the potential for improved image fusion quality, which can contribute to improved accuracy and reliability in the analysis of very high resolution remote sensing data, especially in multi-sensor applications.