Remote Sensing Image Fusion Based On Variational Methods

With the rapid development of remote sensing techniques, remote sensing image fusion has been attracting more and more attention recently. Remote sensing image fusion aims to analyze and combine the remotely sensed images to obtain a single image which is more robust and informative. In this thesis, based on the variational theory, we propose four fusion models. Among them, three models are built for a branch of fusion: Pan-sharpening, which is a process of integrating a low resolution multi-spectral (MS) image with its corresponding panchromatic image to obtain a single high resolution MS image. The main idea of this thesis is to build the related energy functional based on some distinct hypotheses, and to obtain the final fusion result by minimizing the energy. In the numerical scheme, we use the split Bregman iteration to obtain the fusion results more stably, effectively and efficiently. Our main contributions are as follows:●Firstly, we assume that the gradient of the fused image should be close to the most salient gradient in the multisource inputs. Based on this assumption, we develop a new pixel based variational model. In detail, we first extract the gradient of original images and combine them based on a certain rule, then treat this combined gradient as a term. By further take some features such as brightness equalization into account, an energy functional is built for enhancing the fusion effects. The model is implement by using the split Bregman iteration, and compared with many outstanding methods.●Secondly, we assume that the pan-sharpened image should keep the spatial infor-mation, spectral information and spectral correction. Based on this assumption, we build an new variational model, named VP, for Pan-sharpening. We also discuss the existence of minimizer of our energy and describe the numerical procedure based on the split Bregman algorithm. For evaluating fusion results more effectively, we try to classify the existing measures into several categories. To verify the effectiveness of VP model, we compare it with some state-of-the-art schemes using QuickBird and IKONOS data. The results demonstrate the effectiveness and stability of VP. Besides, the computation efficiency comparison with other variational methods also shows that VP model is remarkable.●We introduce the Framelet theory and propose two Pan-sharpening models, termed FP and VFP, based on the Framelet framework. The FP model is a coefficients choosing model, this model is simple and effective, but its results is unique which do not suitable for multifarious applications. To overcome this drawback, by combining the VP and other three fusion requirements, we build a Framelet based variational model:VFP. The alternating iteration algorithm and split Bregman iteration is further introduced to improve the numerical effect. We present the results of the two methods on the QuickBird and IKONOS images, and compare them with existing methods qualitatively and quantitatively. The comparison results demonstrate the superiority of our methods. Finally, we analyze and compare the VP and VFP, and show the advantages and disadvantages and specific scope of each model. The results show that VP outperforms VFP in low noise image, while VFP outperforms VP in high noise image.