The PAN and MS image fusion algorithm based on adaptive guided filtering and gradient information regulation

In recent years, with the improvement in the accuracy of remote sensing image classification and target recognition, the feature level fusion technology of remote sensing images has attracted much attention and become a research hotspot. However, this kind of fusion technology is not as mature as pixel-level fusion technology, and there are still many problems to be solved. This paper proposes a multi-spectral (MS) and panchromatic (PAN) image fusion algorithm based on adaptive textural feature extraction and information injection regulation. The fusion algorithm includes two stages. The first stage extracts the textural details of high-resolution PAN images. In this stage, based on the sensitivity of the remote sensing images to the gray-level co-occurrence matrix (GLCM), an adaptive guided filter (AGIF) scheme for remote sensing images based on the GLCM is proposed. The feature information of the textures and details of the PAN image was fully extracted. The second stage injects the extracted feature information of the PAN image into an MS image. In this stage, a decision map based on the MS image gradient domain and a weighted matrix based on the gradient entropy measure were proposed in order to, respectively, realize the adaptability of the feature injection location selection and regulate the intensity of the injected information to the MS image. This ensures the rationality of the injection of the textural information and avoids noise, patches and other information interference. The proposed algorithm has the advantages of fully extracting the textural features of high resolution PAN images, adaptively adjusting the injection position and intensity when injecting the feature information into an MS image, and providing the fused image with clear features. On the premise of effectively maintaining the spectral information quality, the spatial resolution of the fused image is improved. A large number of simulation experiments verify the effectiveness of the proposed method. (C) 2020 Elsevier Inc. All rights reserved.