Multi-spectral Image Fusion Method Based On Wavelet Transform

With the development of modern remote sensing technology, more and more multi-resolution, multi-temporal and multi-spectral remote sensing image data are provided by different kinds of remote sensors. To get a more correct, clear and complete knowing about the target, it is urgent for us to find a way to synthesize different kinds of image data. So the technology of image fusion appeared. Image fusion is an integration of multiple images obtained from one sensor or multiple sensors. In the integrating image many information for multiple original images are contained to analyze and judge the objects more accurately and perfectly. This paper focuses on image fusion between multi-spectral images and panchromatic images. Using a wavelet analysis method with good signal processing and image processing traits, image fusion based on wavelet transform is emphasized to talk about. Wavelets developed from the 1980's is a new branch of Mathematics, because of its good properties, so it has been widely developed and applied especially in signal processing and image processing. The applications of wavelets in image processing are discussed in this paper. In this paper, a survey for the image fusion is given, and the history, present situation and development of the image fusion research are introduced; and we introduce the basic theory of wavelet analysis. After having known the basic concept and theory, we focus on the problem of image fusion based on wavelet analysis. In the Chapter 4, a new weighting technique is developed based on wavelet transform for the fusion of a high spatial resolution image and a low-resolution, multi-spectral image .This method improves a standard wavelet merger for merging the lower frequency components of a multi-spectral image and its high spatial resolution image by means of local deviation rules with weighting average. And then the merged image is reconstructed by an inverse wavelet transform using the fused approximation and details from the high spatial resolution image. In the Chapter 5, a multi-spectral images fusion algorithm is proposed based on wavelet transform and features of human vision system. First perform a wavelet multi-scale decomposition of each source image. Then a new fusion regular is presented based on hvs: corresponding high (low) frequency components are divided into several blocks, and contrast error of every block is calculated, an adaptive threshold selection is proposed to decide which should be used to construct the new high (low) frequency components. Finally, the fused image is obtained by taking inverse wavelet transform. And threshold is discussed in this paper. And we give the result of the experiment in the end of each two chapters. The experimental results show that the new method presented is clearly better in not only preserving spectral and improving spatial presentation, but also avoiding mosaic occurs . The fusion scheme is effective.