Bidimensional Empirical Mode Decomposition for the fusion of multispectral and panchromatic images

Abstract

Currently available image fusion techniques applied to the merging of fine resolution panchromatic and multispectral images are still not able to minimize colour distortion and maximize spatial detail. In this study, a new fusion method, based on Bidimensional Empirical Mode Decomposition (BEMD), is proposed. Unlike other multiresolution analysis tools, such as the discrete wavelet transform (DWT), which normally examines only horizontal, vertical and diagonal orthonormal details at each decomposed scale, the BEMD produces a fully two-dimensional decomposition of the panchromatic and multispectral images, based purely on spatial relationships between the extrema of the image. These are decomposed into a certain level of Intrinsic Mode Functions (IMFs) and residual images with the same number of columns and rows as the original image. In consequence, by injecting all the IMF images from the panchromatic image into the residue of the corresponding multispectral image, the fusion image may be reconstructed. The fusion results are evaluated and compared with other popular methods in terms of both the visual examination and the quantitative assessment of the merged images. Preliminary results show that BEMD is optimal and provides a delicate balance between spectral information preservation and enhancement of spatial detail.

Multi-source remote sensing data fusion: status and trends

“…To improve the spectral quality, the high-pass details are injected proportionally to the low-pass MS components in such a way that the fused MS pixel vector is proportional to that before fusion. Liu et al (2007) proposed a fusion method using the BEMD to produce a certain level of intrinsic mode functions (IMF) and residual images of the original PAN and MS images based purely on spatial relationships between the extrema of the image. By injecting all IMF images from the PAN image into the residue of the corresponding MS image, the fusion image can be reconstructed.…”

“…The MRA-based fusion techniques (Amolins et al 2007) adopt multi-scale decomposition methods such as multi-scale wavelets (Nu´n˜ez et al 1999), Laplacian pyramids (Aiazzi et al 2002) or bi-dimensional empirical mode decomposition (BEMD; Liu et al 2007) to decompose MS and PAN images with different levels. They derive spatial details that are imported into finer scales of the MS images.…”

Pyramid-based image empirical mode decomposition for the fusion of multispectral and panchromatic images

“…From the visual aspect, the experimental results show that the EMD method is better than Wavelet and PCA method. Liu et al [21] used a bidimensional EMD method in image fusion; the results demonstrate that the EMD method may preserve both spatial and spectral information. The authors also indicated that the two-dimensional EMD is a highly time-consuming process.…”

Remote Sensing Image Fusion Using Bidimensional Empirical Mode Decomposition and the Least Squares Theory

“…It has been applied to many applications in image processing, such as image compression, image analysis. Bidimensional Empirical Mode Decomposition (BEMD) [13] is EMD's generalization in two-dimensional space, and has been applied to merge MS images and Pan image [14] [15] [16]. Liu et al [14] utilized BEMD to decompose MS images and Pan image into IMFs and residue respectively, and then substituted the MS images' IMFs with pan image's IMFs, and finally added all IMFs of pan image with residue of MS images respectively to construct fusion images.…”

“…Bidimensional Empirical Mode Decomposition (BEMD) [13] is EMD's generalization in two-dimensional space, and has been applied to merge MS images and Pan image [14] [15] [16]. Liu et al [14] utilized BEMD to decompose MS images and Pan image into IMFs and residue respectively, and then substituted the MS images' IMFs with pan image's IMFs, and finally added all IMFs of pan image with residue of MS images respectively to construct fusion images. In order to increase the computing speed, Dong et al [16] applied IHS transform to calculate intensity component of MS images, and decomposed intensity component and Pan image via BEMD, and then reconstructed new intensity component through substitution of intensity component's IMFs with IMFs of Pan image, and finally completed image fusion via inverse IHS transform.…”