25 October 2016 Comparison and evaluation on image fusion methods for GaoFen-1 imagery
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Proceedings Volume 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control; 101571V (2016) https://doi.org/10.1117/12.2246695
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China
Abstract
Currently, there are many research works focusing on the best fusion method suitable for satellite images of SPOT, QuickBird, Landsat and so on, but only a few of them discuss the application of GaoFen-1 satellite images. This paper proposes a novel idea by using four fusion methods, such as principal component analysis transform, Brovey transform, hue-saturation-value transform, and Gram-Schmidt transform, from the perspective of keeping the original image spectral information. The experimental results showed that the transformed images by the four fusion methods not only retain high spatial resolution on panchromatic band but also have the abundant spectral information. Through comparison and evaluation, the integration of Brovey transform is better, but the color fidelity is not the premium. The brightness and color distortion in hue saturation-value transformed image is the largest. Principal component analysis transform did a good job in color fidelity, but its clarity still need improvement. Gram-Schmidt transform works best in color fidelity, and the edge of the vegetation is the most obvious, the fused image sharpness is higher than that of principal component analysis. Brovey transform, is suitable for distinguishing the Gram-Schmidt transform, and the most appropriate for GaoFen-1 satellite image in vegetation and non-vegetation area. In brief, different fusion methods have different advantages in image quality and class extraction, and should be used according to the actual application information and image fusion algorithm.
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Ningyu Zhang, Ningyu Zhang, Junqing Zhao, Junqing Zhao, Ling Zhang, Ling Zhang, } "Comparison and evaluation on image fusion methods for GaoFen-1 imagery", Proc. SPIE 10157, Infrared Technology and Applications, and Robot Sensing and Advanced Control, 101571V (25 October 2016); doi: 10.1117/12.2246695; https://doi.org/10.1117/12.2246695
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