Several studies have already shown that remote sensing imagery would provide valuable information for area surveillance missions and activity monitoring and that its combination with contextual information could significantly improve the performance of target detection/target recognition (TD/TR) algorithms. In the context of surveillance missions, spaceborne synthetic aperture radars (SARs) are particularly useful due to their ability to operate day and night under any sky condition. Conventional SARs operate with a single polarization channel, while recent and future spaceborne SARs (Envisat ASAR, Radarsat-2) will offer the possibility to use multiple polarization channels. Standard target detection approaches on SAR images consist of the application of a constant false alarm rate (CFAR) detector and usually produce a large number of false alarms. This large number of false alarms prohibits their manual rejection. However, over the past ten years a number of algorithms have been proposed to extract information from a polarimetric SAR scattering matrix to enhance and/or characterize man-made objects. The evidential fusion of such information can lead to the automatic rejection of the false alarms generated by the CFAR detector. In addition, the aforementioned information can lead to a better characterization of the detected targets. In the case of more challenging backgrounds, such as ground-based target detection, the use of higher level information such as context can help in the removal of false alarms. This paper will discuss the use of polarimetric information for target detection using polarimetric SAR imagery as well as the benefit of contextual information fusion for ground-based target detection.