As the important technology of remote sensing, surface target detection aims to obtain the information of the surface target, such as water, construction, vegetation and other interesting targets, through the remote sensing image processing and analysis. However, the pre-collection samples of some targets from the single source images are too few to meet the needs of automatic detection in the multi-scale remote sensing images, so target detection is still a challenge. Focus on the problem, a novel target detection method based on transfer learning using multiple sources for surface target in the remote sensing images is proposed. The most remarkable characteristic of transfer learning is that it can employ the knowledge in relative domains to help perform the learning tasks in the domain of the target. With the use of different sources of knowledge, transfer learning can transfer and share the information between similar domains. The proposed method locates the surface target area firstly, and then makes the target samples from different sources involved in learning. Therefore, the similar knowledge conductive to the target can be obtained. The prior knowledge from the multiple sources is transferred to the new target images for target detection. The experimental results show that effect of surface target detection by the proposed method from multiple sources is better than that from the single source, and the accuracy of detection has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the advantage of our method in the multiple sources.