Objective Remote sensing image classification technology plays a vital role in forestry monitoring operations such as forest resource survey, ecological engineering planning and forest pest and disease control.
Method The work proposes a remote sensing image classification method based on multi-headed self-attentive modules, which uses the convolutional neural network framework ResNet50 as the backbone network of the whole framework. The intermediate layers of the last three bottleneck layers of the ResNet50 network were replaced with multi-headed self-attentive modules, which enable the model to focus on the regions with the highest discrimination and thus improve the classification accuracy. The experiments in this study used three publicly available datasets, RSSCN7, EuroSAT and PatternNet, based on the Pytorch machine learning library, to train and test the framework, and add a comparison experiment with the accuracy of existing classification frameworks. At the same time, different batch sizes were used to train the proposed framework and test the classification effect.
Result Experimental results showed the average recognition rate of the proposed method on the three remote sensing classification datasets reaching 91.30%, 97.88% and 97.37%, respectively, which was better than the existing algorithms based on deep convolutional networks. Also, the total number of parameters of this algorithm was 2.08 × 107, which was also much lower than that of existing algorithms.
Conclusion The results show that the proposed framework is able to achieve higher accuracy in a GPU-accelerated environment, reduce the number of parameters included in the framework, reduce the video memory consumption, and improve the accuracy of the classification results compared with existing remote sensing image classification frameworks.