论文标题
对SAR图像分类的深度学习方法的可解释分析
Explainable Analysis of Deep Learning Methods for SAR Image Classification
论文作者
论文摘要
深度学习方法在合成孔径雷达（SAR）图像解释任务中表现出出色的性能。但是，这些是限制其预测理解的黑匣子模型。因此，为了应对这一挑战，我们利用可解释的人工智能（XAI）方法来进行SAR图像分类任务。具体而言，我们为OpenSarurban数据集上的每种极化格式训练了最先进的卷积神经网络，然后研究了八种解释方法，以分析SAR图像的CNN分类器的预测。这些XAI方法还进行了定性和定量评估，这表明遮挡在最大敏感性方面达到了最可靠的解释性能，但具有低分辨率的解释热图。解释结果为SAR图像分类的黑盒决策的内部机制提供了一些见解。
Deep learning methods exhibit outstanding performance in synthetic aperture radar (SAR) image interpretation tasks. However, these are black box models that limit the comprehension of their predictions. Therefore, to meet this challenge, we have utilized explainable artificial intelligence (XAI) methods for the SAR image classification task. Specifically, we trained state-of-the-art convolutional neural networks for each polarization format on OpenSARUrban dataset and then investigate eight explanation methods to analyze the predictions of the CNN classifiers of SAR images. These XAI methods are also evaluated qualitatively and quantitatively which shows that Occlusion achieves the most reliable interpretation performance in terms of Max-Sensitivity but with a low-resolution explanation heatmap. The explanation results provide some insights into the internal mechanism of black-box decisions for SAR image classification.
