Attention mechanisms have found extensive application in Deep Neural Networks (DNNs), with numerous experiments over time showcasing their efficacy in improving the overall performance of DNNs. Considering the black-box nature of DNNs, it is unclear how attention mechanisms affect model decision-making processes. Therefore, it is of great significance to explore the internal relationship between the focused area of attention mechanisms and the prediction results. For the first time, an explainable framework for the attention mechanism of Synthetic Aperture Radar (SAR) image analytics is proposed, integrating DNN and eXplainable Artificial Intelligence (XAI), which realizes the effective explanation for attention mechanisms across different locations. The framework consists of three parts: the water extraction network which contains the attention mechanism at different locations; the proposed Attention- Gradient Class Activation Mapping (A-GCAM) method for attribution analysis of attention mechanisms; and we invent Category-specific channel Score of Confidence Mapping (CSCM) to perform geo-visualization for the output features of attention mechanisms. Experiments are conducted with the widely used Sentinel-1 system for water detection, in which three attention mechanisms with different characteristics are added to the encoder and decoder in DNNs. The results show that the framework can make the decision-making process of attention mechanisms transparent, thus improving their comprehensiveness and trustworthiness in various tasks and providing a reliable approach to selecting suitable attention mechanisms for the given task.

中文翻译：

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我的注意力在哪里？ SAR 图像水检测中可解释的人工智能探索


注意力机制在深度神经网络 (DNN) 中得到了广泛的应用，随着时间的推移，大量实验证明了它们在提高 DNN 整体性能方面的功效。考虑到 DNN 的黑盒性质，目前尚不清楚注意力机制如何影响模型决策过程。因此，探究注意力机制关注领域与预测结果之间的内在联系具有重要意义。首次提出了合成孔径雷达（SAR）图像分析注意力机制的可解释框架，集成了DNN和可解释人工智能（XAI），实现了对不同地点注意力机制的有效解释。该框架由三部分组成：水提取网络，包含不同位置的注意力机制；提出的用于注意力机制归因分析的注意力梯度类激活映射（A-GCAM）方法；我们发明了类别特定通道置信度映射得分（CSCM）来对注意力机制的输出特征进行地理可视化。使用广泛使用的用于水检测的 Sentinel-1 系统进行了实验，其中在 DNN 的编码器和解码器中添加了三种具有不同特征的注意机制。结果表明，该框架可以使注意力机制的决策过程变得透明，从而提高其在各种任务中的全面性和可信度，并为给定任务选择合适的注意力机制提供可靠的方法。