Hybrid-Gird:An explainable method for fine-grained classification of remote sensing images
At present,the deep neural network for targets fine-grained classification based on remote sensing images has been widely applied in military and civilian fields.The"black box"problem of deep learning network models still makes it difficult for people to understand the decision-making basis of the network in fine-grained classification tasks.This not only limits the possibility of deep neural networks optimizing and improving through feedback guidance,but also makes them unable to be fully trusted by humans and applied in important fields such as military and medical fields.Thus,how to carry out explainability research on its internal decision-making mechanism is the key problem for the current fine-grained algorithm to further improve the credibility of decision-making basis.This article summarized the commonly used explainable methods in existing fine-grained classification networks for remote sensing image targets.On this basis,a mathematical model for the problem of target fine-grained classification is established.It also models the fine-grained classification task of remote sensing image targets based on game competition theory,analyzes the applicability of explainable methods such as IG,SmoothGrad,and Grad CAM on the fine-grained classification network of remote sensing image targets,and proposes a scale adaptive method for analyzing the explainability of essential features of fine-grained classification of targets,that is,Hybrid Grid.The fusion algorithm of pixel level and local feature relationships is used to improve the accurate description ability of essential features of targets that support network decision-making.The typical explaining methods involved in this article were evaluated using indicators such as Average Drop,Coherence,Complexity,ADCC,and Deletion and precision loss,confirming the accuracy of the proposed method.The experimental results show that the Hybrid-Grid proposed in this paper achieves a score of 78.87 on the quantitative evaluation index of ADCC for target fine-grained classification networks,which is significantly improved compared to Score-CAM.Compared with the explanation results of SmoothGrad and Grad-CAM,our method performed the best in deletion and accuracy loss experiments,resulting in a loss of 16.92％,1.61％,and 17.21％in the Top-1 accuracy,Top-5 accuracy,and F1 score of EFM Net,respectively.This proves that the Hybrid Grid accurately explains the essential feature of the target that contributes the most to fine-grained classification network decision-making.Based on several typical fine-grained classification networks,the feasibility and universality of the explaining method proposed in this paper have been confirmed.The explaining method proposed in this article can more accurately reveal the decision feature basis of the current target fine-grained classification network.Future work will further enhance the visualization and interpretation accuracy of the essential features of the target,assisting in improving the classification performance and decision-making credibility of fine-grained classification networks.
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