The purpose of this study is the assessment of polarimetric SAR and hyperspectral images for concealed target detection in forest areas. In fact, this paper is an introduction to fusion the two data in a target detection algorithm. The proposed method is using the polarimetric and the spectral signatures. A RADARSAT-2 image and a Hyperion image of the city San Francisco were chosen for evaluation of the proposed method. The four targets were selected in the forest and the outer areas. Three pattern recognition methods included SCM, SAM, and ED were used to determine the matching rate between the two signatures. Results reported the SAM and SCM matching methods, have a better performance than ED in polarimetric signatures. In addition, the SAM and ED have a better performance than SCM in spectral signatures. As the results polarimetric SAR data detect concealed target with greater certainty. However, hyperspectral images have the well reaction against concealed targets in forest areas.