UAV remote sensing image extraction of single tree crown of Chinese fir based on Mask R-CNN and transfer learning
[Objective]The UAV image automatically extracts canopy for precise information.[Method]In this study,single tree crown extraction method from UAV images based on Mask R-CNN and transfer learning was proposed.Firstly,the optimized Mask R-CNN instance segmentation model based on faster R-CNN was selected,and the optimal feature extraction network was chosen between ResNet50 and ResNet101.Secondly,transfer learning was introduced to train with Mask R-CNN together,combined with the guiding role of transfer learning to reduce training time and improve training accuracy.[Result]The results showed that the overall accuracy of the Mask R-CNN model was 93.59%,the user accuracy,F1 score and mean average precision were 65.46%,76.05%and 0.31,respectively.After adding transfer learning,the user accuracy of Mask R-CNN model was increased by 29.53%,the F1 score was increased by 19.63%,and the average accuracy was increased by 0.21.In the Mask R-CNN model with ResNet50 and ResNet101 as feature extraction networks,the average values of overall accuracy,user accuracy,F1 score and mean average precision of the ResNet50 + Mask R-CNN model were 96.94%,95.57%,96.17%and 0.54,respectively.The average values of overall accuracy,user accuracy,F1 score and mean average precision of the ResNet101 + Mask R-CNN model were 96.20%,94.41%,95.19%and 0.49,respectively.The R2of ResNet50 + Mask R-CNN model loaded with transfer learning in predicting east-west crown width,north-south crown width,crown area and quadrat canopy density were 0.87,0.84,0.93 and 0.83,respectively.[Conclusion]The method based on Mask R-CNN and transfer learning proposed in this study obtains more accurate results of tree crown parameters,which provides a fast and efficient solution for tree resource assessment in UAV images.
万方数据
维普
