The Research Of Multi-spectral And Panchromatic Remote Sensing Image Fusion Algorithm

It is desirable to have the best possible spatial resolution in order to detect fine features on the Earth’s surface for most remote sensing applications. Moreover, a high spectral resolution is also required to discriminate among different ground covers. The problem with the images provided by modern satellites is that they have either high spatial resolution, i.e., panchromatic image or high spectral resolution, i.e., multi-spectral images. However, for certain applications, there is a need to improve the spatial resolution of the multi-spectral (MS) images. This process is usually called pan-sharpening. This process includes the synthesis of panchromatic (PAN) images at a higher spatial resolution by the help of an alternate high-resolution image obtained by another modality. Image fusion aims at obtaining information of a greater quality, although the exact definition of "greater quality" will depend on the "application". The main obligation of an image fusion algorithm in remote sensing is both the spectral and spatial fidelity of the data. However, recent comparative analyses of different image fusion techniques have shown that, although the quality of fused image has been greatly improved, but the problem is still the need for further attention and study.The research of this paper is focused on the MS and PAN remote image fusion. The main research works are carried out as the following.(1)We analyze the research background, significance and research status of the MS and PAN image fusion, and list some of the problems in the field of image fusion, and describe the future direction of development.(2)We describe some of the most commonly used quality assessment methods of image fusion field and several traditional algorithms about image fusion. These contain the subjective and objective quality evaluation methods, and image fusion algorithm based on HIS transform, and image fusion algorithm based on PCA transform, and image fusion algorithm based on lαβ transform, and image fusion algorithm based Brovey transform. Then, we do some experiments about these traditional image fusion algorithms and analyze the results of the experiments. Last, we evaluate experiments according to the quality evaluation criteria, and introduce a framework based on the integration of HIS transform.(3) The research of shift-invariant discrete wavelet transform (SIDWT). First, we introduce traditional wavelet transform, and then analyze the advantages and disadvantages of the wavelet transform with SIDWT. Then according to the SIDWT theory, we propose a new image fusion method:SIDWT fusion method based the regional characteristics. We detail the rules fusion, and Draw the flowchart of this new method. Last, to verify the effectiveness of the algorithm, we do some experiments, and analyze it, and compare with others.(4) We elaborate on the Laplacian transform and gradient-based the structural similarity (GSSIM) theory. First, we introduce the frame of the Laplacian image fusion algorithm and the effectiveness of GSSIM. Second, we propose a new method of the Laplacian image fusion algorithm based on genetic algorithm with GSSIM object function, and detail the fusion rules of this new method, and the first layer using a genetic algorithm. The fitness function (fitness function) of genetic algorithm in the objective function is detailed. Selection, crossover and mutation factor are brief described. To the other tower levels, the area of maximum energy is used. Last, to verify the effectiveness of the algorithm, we do some experiment, and analyze it, and compare with others.