An image segmentation method based on wavelet energy and a fuzzy C-means comprises the following steps: calculating an image two-layer wavelet energy coefficient in a window by adopting two-dimensional wavelet transformation, and establishing energy characteristics; calculating an SAR image pixel space distance constraint factor and a gray distance constraint factor, and defining a weighted segmentation fuzzy factor; introducing a weighted segmentation fuzzy factor into an objective function of the FCM, establishing a kernel function fuzzy C-means segmentation method based on pixel gray scaleand spatial position information, and deriving a kernel function required by the fuzzy C-means segmentation method; Calculating an updated membership degree and a segmentation class center analytic expression; Iteratively calculating a final membership degree and a segmentation category center; and classifying the SAR images to a corresponding category with the maximum membership degree pixel by pixel to complete image segmentation. On the basis of SAR image wavelet energy feature extraction, pixel gray level and position information are considered, fuzzy C-means segmentation is carried out based on a weighted kernel function, and the method has robustness for speckle noise.