The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle (UAV) low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning (DTCLE) was proposed. First, linear features (roads and ridges etc.) were excluded based on Deep Convolutional Neural Network (DCNN). Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognition for cultivated land information extraction (ECLE). The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 (of extracting cultivated land) with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 90.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.
Unmanned aerial vehicle Cultivated land Deep convolutional neural network Transfer learning Information extraction
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This work was supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 2013SCU11006), the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grant NO. DM2014SC02); and the Key Laboratory of Geospecial Information Technology, Ministry of Land and Resources of China (Grant NO. KLGSIT2015- 04). Finally, Lu H wants to thank, in particular, the invaluable support received from Fu X over the years.
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