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RSS FeedsRemote Sensing, Vol. 14, Pages 4887: Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery (Remote Sensing)

 
 

30 september 2022 12:56:34

 
Remote Sensing, Vol. 14, Pages 4887: Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery (Remote Sensing)
 


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.


 
109 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 4888: Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions (Remote Sensing)
Remote Sensing, Vol. 14, Pages 4892: Detection and Counting of Corn Plants in the Presence of Weeds with Convolutional Neural Networks (Remote Sensing)
 
 
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