A Hyperparameter-Free Attention Module Based on Feature Map Mathematical Calculation for Remote-Sensing Image Scene Classification

Remote-sensing scene classification (RSSC) is crucial for remote-sensing image interpretation and has become a research hotspot in recent years. However, the high complexity of remote-sensing scenes causes most RSSC models to fail to accurately capture key objects, resulting in low classification accuracy. Meanwhile, it is intractable to effectively distinguish similar scenes, such as forest and meadow, whose semantic labels are mainly determined by wide-scale features. In addition, existing remote-sensing attention mechanisms are heuristic settings, which require expert knowledge and extensive experiments. To solve the above problems, a novel plug-and-play hyperparameter-free attention module (HFAM) based on feature map mathematical calculation is proposed in this work. HFAM uses statistical indicators to quantitatively characterize the fluctuations of feature maps that can accurately locate key features and distinguish different scenes, alleviating the problems of intraclass diversity and interclass similarity. Moreover, HFAM adaptively acquires attention weights by performing simple mathematical calculations on the feature maps, which solves the problem of difficult adjustment of hyperparameters. Our proposed HFAM can be expediently inserted into the existing ConvNet models without increasing the number of model's parameters. Extensive contrast experiments with several famous plug-and-play attention modules on three mainstream datasets reveal the superiority of our HFAM in accuracy, number of parameters, and calculation amount. Moreover, compared with state-of-the-art methods, it also demonstrated considerable competitiveness.