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.

近年来，随着遥感图像分类和目标识别精度的提高，遥感图像的特征级融合技术备受关注，成为研究的热点。但是，这种融合技术还不如像素级融合技术成熟，仍然有很多问题需要解决。提出了一种基于自适应纹理特征提取和信息注入调节的多光谱（MS）和全色（PAN）图像融合算法。融合算法包括两个阶段。第一阶段提取高分辨率PAN图像的纹理细节。在此阶段，基于遥感图像对灰度共生矩阵（GLCM）的敏感度，提出了一种基于GLCM的遥感影像自适应导引滤波器（AGIF）方案。充分提取了PAN图像的纹理特征和细节信息。第二阶段将所提取的PAN图像的特征信息注入MS图像中。在此阶段，提出了基于MS图像梯度域的决策图和基于梯度熵测度的加权矩阵，以分别实现特征注入位置选择的适应性和调整注入信息的强度。 MS图片。这确保了纹理信息注入的合理性，并避免了噪音，斑点和其他信息干扰。该算法具有充分提取高分辨率PAN图像纹理特征的优点，将特征信息注入MS图像时，自适应调整注入位置和强度，并为融合图像提供清晰的特征。在有效保持光谱信息质量的前提下，提高了融合图像的空间分辨率。大量的仿真实验验证了该方法的有效性。