This paper presents a method for ports detection based on the framework of feature level fusion. Bearing in mind the fact that parallel lines and rectangular corners are main features in most ports, and ports are large scale man-made objects, these features are firstly extracted from high-to-moderate resolution optical satellite imagery. Taking account for the balance of data acquisition and spatial resolution, SPOT panchromatic image is used for such feature extraction. Considering the whether conditions in coastal area, which is characterized by rainy and cloudy climate, Radarsat image with the similar spatial resolution as SPOT panchromatic is used to extract linear features along coastal line. Since ships and boats are typical objects that can be easily detected in radar image, these are considered to be supplemented features for ports detection. All extracted features are associated under the framework of feature level fusion. The whole procedure can be described as follows: the first step is preprocessing the input images, mainly histogram stretching to SPOT image for visual quality improvement and filtering to radar image for denoising speckles. Then registration between SPOT and Radarsat image is carried out. Since Radarsat image is used mainly for coastal line extraction and ship detection, rigorous geometric processing is omitted since little attention will be paid to land area. Common polynomial model is used for co-registration with Ground Control Points manually selected from both images. Due to feature level fusion method is adopted, registration accuracy is not as a key factor as in pixel level fusion. The next step will be linear features and rectangular corners detection both in optical and radar image. The detected linear features are then fitted by least mean-square-error algorithm. All the detected features are associated by simply weighted mean algorithm, with different weights to features from optical and radar images. An automatic ports detection system based on the abovementioned procedure is developed. Experiments show that most ports can be detected by our method.