This article presents an image fusion technique based on PCA and fuzzy logic. The framework of the proposed image fusion technique is divided into the following main phases: Preprocessing phase Feature extraction based on principal component analysis Image fusion based on fuzzy set Final image reconstruction Figure (1) shows the structure of the proposed image fusion and its phases.Fig. 1. The proposed approach of image fusion stepsA. Preprocessing Phase This phase consists of three steps of registration, resampling and histogram matching. Below 1) Registration: Image fusion is the approach of combining two or more images of the same scene to obtain the most informative image. Image data is recorded by sensors on satellites; they may contain geometric errors caused by the rotation of the earth during image collection. Then the images must be recorded. Registration is the preprocessing step in the fusion framework. Registration consists of superimposing two or more images of the same scene taken at different times or by different sensors. Registration is a crucial step in many image analysis tasks such as image fusion, change detection, etc. In this document; the ground control point technique is used to register the MS images to the Pan image as a reference image. The ground control points method is described as points on the earth of known location used as a georeference for the scene image. All MS images in this document are registered, and the Pan image is used as a reference image; see figure 2. (a) The MS image before registration. (b) The MS image after registration. Fig. 2. The impact of registration on satellite images2) Resampling: Resampling is a critical step in preprocessing the...... half of the document...... CA is used to calculate the first component analysis to redundant the information and focus on pc1 which has the common spatial information in multispectral images. While the specific spectral information for each multispectral image is found on the other PCs. the multispectral images are used as input data for PCA to obtain pc1 which is used as input for the fuzzy set. Algorithm (1) shows the main steps of principal component analysis. Algorithm 1: The principal component analysis algorithm1: Input: the MS images (3 bands) in matrix form.%Perform PCA using covariance.2: data - MxN matrix of input data3: Reshape 3 bands into 1* (m*n)4: Subtract the mean5: Calculate the covariance matrix6: Get eigenvalues ​​and eigenvectors of the covariance of the matrix7: Retrieve the first principal component (PC1)8: Output: principal component (PC1, PC2 and PC3)