基于多特 与注 力机制深度学习的 高分辨率影 线虫病树识

In order to improve the monitoring efficiency of pinewood nematode disease trees and reduce their losses in the forestry production, a method of identifying pinewood nematode disease trees using high-resolution images based on multi-feature extraction and attention mechanism deep learning was proposed.Firstly, multi-features such as spectral features and spatial features of pine nematode diseased trees on high-resolution remote sensing images were extracted, then the Relief feature selection algorithm was performed by taking the top eigth features of feature weights for identifying diseased trees, and the Difference Vegetation Index ( DVI) , 12 and 13 components of OHTA color model was selected.The spectral features of diseased trees and non-diseased trees were found to be more suitable, and then the DBscan spatial clustering algorithm was applied to cluster the spectral feature recognition results to obtain the set of suspected diseased tree image elements, and the average detection accuracy of this multi-feature recognition method for identifying diseased trees was 78.23%.The VGG-S (simplification) and VGG-A (attention module) neu¬ral networks were established using the VGG (visual geometry group network) neural network model as a reference, and the diseased tree sample set and the non-diseased tree sample set generated by manual interpretation were used as the reference and non-diseased tree sample sets generated by manual interpretation were used as their training samples.The above two different methods were used to identify the suspected diseased tree image set, in which the average de¬tection accuracy of VGG-S was 82.61% and the average detection accuracy of VGG-A was 85.45%.The results showed that the combination of multi-features and VCKj-A was used to identify pine nematode disease trees on high-resolution remote sensing images with high recognition accuracy.In the experimental process, the light intensity of the image was insufficient, resulting in some forest trees cannot be identified on the image, thus leading to a high rate of missed detection.However, due to the flexibility of the high-resolution remote sensing image acquisition method, even if the spectral characteristics obtained from the experimental data are different under different shooting conditions and thus may lead to changes in the results, it is possible to quickly acquire remote sensing image data by means of aerial photography using unmanned aerial vehicles, etc., and the images of higher quality can be achieved from the acquired image data for identification.