Affiliation:
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
Abstract
Citrus is an important commercial crop in many areas. The management and planning of citrus growing can be supported by timely and efficient monitoring of citrus-growing regions. Their complex planting structure and the weather are likely to cause problems for extracting citrus-growing regions from remote sensing images. To accurately extract citrus-growing regions, deep learning is employed, because it has a strong feature representation ability and can obtain rich semantic information. A novel model for extracting citrus-growing regions by UNet that incorporates an image pyramid structure is proposed on the basis of the Sentinel-2 satellite imagery. A pyramid-structured encoder, a decoder, and multiscale skip connections are the three main components of the model. Additionally, atrous spatial pyramid pooling is used to prevent information loss and improve the ability to learn spatial features. The experimental results show that the proposed model has the best performance, with the precision, the intersection over union, the recall, and the F1-score reaching 88.96%, 73.22%, 80.55%, and 84.54%, respectively. The extracted citrus-growing regions have regular boundaries and complete parcels. Furthermore, the proposed model has greater overall accuracy, kappa, producer accuracy, and user accuracy than the object-oriented random forest algorithm that is widely applied in various fields. Overall, the proposed method shows a better generalization ability, higher robustness, greater accuracy, and less fragmented extraction results. This research can support the rapid and accurate mapping of large-scale citrus-growing regions.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Major Project of Science and Technology of Yunnan Province
Subject
General Earth and Planetary Sciences
Reference55 articles.
1. New Geographic Distribution and Molecular Diversity of Citrus Chlorotic Dwarf-Associated Virus in China;Yang;J. Integr. Agric.,2022
2. Channel Attention-Based Temporal Convolutional Network for Satellite Image Time Series Classification;Tang;IEEE Geosci. Remote. Sens. Lett.,2022
3. Csillik, O., Cherbini, J., Johnson, R., Lyons, A., and Kelly, M. (2018). Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks. Drones, 2.
4. Wei, P., Ye, H., Qiao, S., Liu, R., Nie, C., Zhang, B., Song, L., and Huang, S. (2023). Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm. Remote Sens., 15.
5. Detecting Abandoned Citrus Crops Using Sentinel-2 Time Series;Estornell;A Case Study in the Comunitat Valenciana. ISPRS J. Photogramm. Remote Sens.,2023