The percentage of cloud coverage is generally considered the most important factor to affect the availability of optical satellite images. Consequently, an effective cloud assessment process can not only offer general users a guideline for image browsing, but also provide valuable information for orbit planning of ground receiving station. At present, the cloud coverage of Formosat-2 data is visually examined and manually estimated by the operators on the quick-look images. The quick-look images basically are the reduced images created by satellite data receiving system for browsing purpose. As to the current estimation of cloud coverage, every quick-look image is divided into 8 portions and the cloud coverage for each portion is indicated with a cloud index with 5 grades. Apparently, the visual and manual cloud assessment method is labor-intensive, time-consuming, and dependent on the operator's experience. Furthermore, the existing 5-grade cloud index is inadequate to describe the accurate percentage of cloud coverage. Thus, there is an urgent need for developing an automatic approach for cloud assessment. In this study, a two-stage scheme based on image segmentation and fractal theory is used to automatically extract cloud pixels form quick-look images. The experimental results indicate that the cloud areas extracted by proposed scheme are very similar to the manual interpretation. In addition, when the automatically extracted cloud coverage is transferred to the existing cloud index, a good agreement between our automatic approach and current manual estimation is achieved.