SAR and Multispectral Remote Sensing Image Fusion Method Using Shearlet Transform

In terms of conventional methods for SAR image and multispectral image fusion can't integrate and reserve good spectral information and spatial resolution at the same time, a new fusion method based on the difference of imaging mechanism of SAR and multispectral images is proposed in this paper. Firstly, the original image is decomposed by Shearlet transform, and the high frequency and low frequency components are obtained respectively. The two components contain different detailed information of the image. Shearlet transform decomposes the image into multi-scale and multi-directional sub-band coefficients, which contain different image features. In addition, Shearlet inverse transform has good image reconstruction capability. And then, according to that the low frequency coefficient and the high frequency coefficient represent different meanings, we design the different fusion rule for them. The fusion rules of low frequency coefficients based on region energy and the high frequency coefficient based on improved pulse coupled neural network are designed. Finally, an information-rich image is obtained by inversing Shearlet transform. Therefore, the fusion results are richer and contain more spatial detail information and spectral information. In order to verify the effectiveness of the proposed method, a test is carried out with data from TerraSAR-X and Landsat5-TM, and the result shows that the proposed method is effective in improving the spatial resolution and keeping more spectral information. Compared with the methods of wavelet transform, contourlet transform, and NSCT transform, this method has a significant improvement in spatial information and spectral information. Cross entropy has a margin of improvement of nearly 100%. The correlation coefficient is higher than 25% increase, and the spectral distortion is better than 40% increase.