Accurate Extraction of Apple Orchard on the Loess Plateau Based on Improved Linknet Network

Abstract

The rapid increasing of apple planting area on the Loess Plateau has exerted an important influence on the regional eco-hydrology and socio-economic development. However, the orchards in this area are small and complex, and there are only county or city scale statistical data, lack of actual spatial distribution information. To this end, for the extraction of apple orchards on the Loess Plateau, in this study, a professional dataset of low-altitude remote sensing images acquired by unmanned aerial vehicle was firstly established. The R_34_Linknet network and other five commonly used deep learning semantic segmentation models SegNet, FCN_8s, DeeplabV3+, UNet and Linknet were applied to the spatial distribution extraction of apple orchards on the Loess Plateau, and the best-performing model was R_34_Linknet, with a F1 score of 87.1%, a pixel accuracy (PA) of 92.3%, an mean intersection over union (MioU) of 81.2%, a frequency weighted intersection over union (FWIoU) of 85.7%, and the mean pixel accuracy (MPA) was 89.6%. The spatial pyramid pool structure (ASPP) and R_34_Linknet network was combined to expand the receptive field of the network and get R_34_Linknet_ASPP network, and then ASPP structure was improved. Combining the spatial pyramid pooling (ASPP) with the R_34_Linknet network to expand the receptive field of the network and obtain a R_34_Linknet_ASPP network; Then the ASPP structure was improved to get a R_34_Linknet_ASPP+ network. The performance of the three networks were compared. R_34_Linknet_ASPP+ got the best performance, with 86.3% for F1, 94.7% for PA, 82.7% for MIoU, 89.0% for FWIoU, and 92.3% for MPA on the test set. The accuracy of apple orchard extraction in Wangdonggou, Changwu County and Tongji Village, Baishui County using R_34_Linknet_ASPP+ were 94.22% and 95.66%, respectively. In Wangdonggou, it was 1.21% and 0.58% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. In Tongji village, it was 1.70% and 0.90% higher than R_34_Linknet and R_34_Linknet_ASPP, respectively. The results show that the proposed R_34_Linknet_ASPP+ method can extract apple orchards accurately, the edge treatment of apple orchard plots is better, the method can be used as the technical support and theoretical basis for research on the spatial distribution mapping of apple orchards on the Loess Plateau.

Keywords

• uav remote sensing
• apple orchard extraction
• deep learning
• loess plateau
• transfer learning
• residual neural network
• semantic segmentation