Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery

The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale dif...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 19; p. 4887
Main Authors Tang, Ziqian, Sun, Yaqin, Wan, Guangtong, Zhang, Kefei, Shi, Hongtao, Zhao, Yindi, Chen, Shuo, Zhang, Xuewei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.10.2022
Subjects
Accuracy
Agricultural production
Algorithms
Benchmarks
Classification
Corn
Crops
Deep learning
Feature extraction
GaoFen-2
Image segmentation
Insurance
Lodging
Machine learning
Neural networks
Rain
Remote sensing
Rice
Satellite imagery
Satellites
semantic segmentation
Semantics
Sensitivity enhancement
Sensors
Support vector machines
Unmanned aerial vehicles
Vegetation index
Wheat
wheat lodging
Winter wheat
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Summary:The timely and accurate detection of wheat lodging at a large scale is necessary for loss assessments in agricultural insurance claims. Most existing deep-learning-based methods of wheat lodging detection use data from unmanned aerial vehicles, rendering monitoring wheat lodging at a large scale difficult. Meanwhile, the edge feature is not accurately extracted. In this study, a semantic segmentation network model called the pyramid transposed convolution network (PTCNet) was proposed for large-scale wheat lodging extraction and detection using GaoFen-2 satellite images with high spatial resolutions. Multi-scale high-level features were combined with low-level features to improve the segmentation’s accuracy and to enhance the extraction sensitivity of wheat lodging areas in the proposed model. In addition, four types of vegetation indices and three types of edge features were added into the network and compared to the increment in the segmentation’s accuracy. The F1 score and the intersection over union of wheat lodging extraction reached 85.31% and 74.38% by PTCNet, respectively, outperforming other compared benchmarks, i.e., SegNet, PSPNet, FPN, and DeepLabv3+ networks. PTCNet can achieve accurate and large-scale extraction of wheat lodging, which is significant in the fields of loss assessment and agricultural insurance claims.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14194887