Corn Residue Covered Area Mapping with a Deep Learning Method Using Chinese GF-1 B/D High Resolution Remote Sensing Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 15; p. 2903
• Main Authors: Tao, Wancheng, Xie, Zixuan, Zhang, Ying, Li, Jiayu, Xuan, Fu, Huang, Jianxi, Li, Xuecao, Su, Wei, Yin, Dongqin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2021
• Subjects: Ablation; Classification; Conditional random fields; Convolution; Corn; corn residue covered area; Crop residues; Data collection; Deep learning; deep semantic segmentation network; Environmental monitoring; GF-1 B/D high resolution remote sensing images; High resolution; Image resolution; Image segmentation; Inhomogeneity; Learning algorithms; Machine learning; Mapping; Neural networks; Organic carbon; Organic matter; Organic soils; Remote monitoring; Remote sensing; Residues; Semantics; Soil erosion; Spatial discrimination; Spatial resolution; Support vector machines; China; Jilin China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13152903

Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurately using remote sensing images can monitor the protection of black soil in regional areas. Considering the inhomogeneity and randomness, resulting from human management difference, the high spatial resolution Chinese GF-1 B/D image and developed MSCU-net+C deep learning method are used to mapping corn residue covered area (CRCA) in this study. The developed MSCU-net+C is joined by a multiscale convolution group (MSCG), the global loss function, and Convolutional Block Attention Module (CBAM) based on U-net and the full connected conditional random field (FCCRF). The effectiveness of the proposed MSCU-net+C is validated by the ablation experiment and comparison experiment for mapping CRCA in Lishu County, Jilin Province, China. The accuracy assessment results show that the developed MSCU-net+C improve the CRCA classification accuracy from IOUAVG = 0.8604 and KappaAVG = 0.8864 to IOUAVG = 0.9081 and KappaAVG = 0.9258 compared with U-net. Our developed and other deep semantic segmentation networks (MU-net, GU-net, MSCU-net, SegNet, and Dlv3+) improve the classification accuracy of IOUAVG/KappaAVG with 0.0091/0.0058, 0.0133/0.0091, 0.044/0.0345, 0.0104/0.0069, and 0.0107/0.0072 compared with U-net, respectively. The classification accuracies of IOUAVG/KappaAVG of traditional machine learning methods, including support vector machine (SVM) and neural network (NN), are 0.576/0.5526 and 0.6417/0.6482, respectively. These results reveal that the developed MSCU-net+C can be used to map CRCA for monitoring black soil protection.