The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. Due to the low spatial resolution of satellite remote sensing images, it is difficult to identify cultivated land of small areal extent in critical regions, which requires image data of high spatial resolution for specific or general cases. Simultaneously, unmanned aerial vehicle (UAV) has been increasingly used for natural resource applications in recent years as a result of their great availabilities, the miniaturization of sensors, and the ability to deploy UAV relatively quickly and repeatedly at low altitudes. But most UAV images lack spectral information and cultivated land information extraction which usually leads to an unsatisfactory result. Based on this, a novel cultivated land information extraction method based on transfer learning (TLCLE) was proposed. Firstly, linear features (roads and ridges, etc.) were rejected based on deep convolutional neural network (DCNN). Secondly, feature extraction method learned from DCNN was used for extracting cultivated land information by introducing transfer learning mechanism. Finally, cultivated land information extraction results were completed by the TLCLE method and eCognition software for cultivated land information extraction (ECLE). The experimental results show that TLCLE can obtain equivalent accuracy to ECLE, and it outperforms ECLE in terms of guaranteeing the integrity and continuity of cultivated land information.