A remote sensing image fusion method is proposed to enhance fusion image quality and fusion efficiency. This method is based on the combination of discrete wavelet and multiscale morphological transform in the IHS color space. First, the three-band multispectral image is converted into the IHS color space to obtain the intensity (I), hue (H), and saturation (S) components. Second, the discrete wavelet transform is used to decompose the I component and PAN images, each of which is decomposed into a set of high-frequency detail images at different scales and one low-frequency approximation image. Then, the dual-channel multiscale morphological transform is constructed based on the idea of à trous algorithm. Upon this, the high-frequency detail images are further decomposed by multiscale morphological gradient transform to extract the edge details information. Two low-frequency approximate images are decomposed by multiscale morphological top-hat and bottom-hat transform to extract the light and dark details information. The decomposition coefficients are fused by designed fusion rules. Finally, the corresponding inverse transform is conducted to reconstruct the fused image. The experimental results have shown that the proposed method performs well, in both subjective and objective evaluations. Compared with other seven hybrid methods popularly applied in image fusion, the proposed method is superior in the 12 indicators. In addition, it has significantly reduced the computational cost in the fusion process. The results have proved that the proposed method not only fully preserves the spectral information of the source MS image but also inherits the rich spatial details of the source PAN image while, achieves high efficiency in image fusion.
Diverse image fusion methods perform differently. Each method has advantages and disadvantages compared with others. One notion is that the advantages of different image methods can be effectively combined. A multiple-algorithm parallel fusion method based on algorithmic complementarity and synergy is proposed. First, in view of the characteristics of the different algorithms and difference-features among images, an index vector-based feature-similarity is proposed to define the degree of complementarity and synergy. This proposed index vector is a reliable evidence indicator for algorithm selection. Second, the algorithms with a high degree of complementarity and synergy are selected. Then, the different degrees of various features and infrared intensity images are used as the initial weights for the nonnegative matrix factorization (NMF). This avoids randomness of the NMF initialization parameter. Finally, the fused images of different algorithms are integrated using the NMF because of its excellent data fusing performance on independent features. Experimental results demonstrate that the visual effect and objective evaluation index of the fused images obtained using the proposed method are better than those obtained using traditional methods. The proposed method retains all the advantages that individual fusion algorithms have.
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