Towards explainable AI for hyperspectral image classification in Edge Computing environments. (October 2022) - De Lucia, Gianluca; Lapegna, Marco; Romano, Diego
Abstract: Several Remote Sensing applications in Hyperspectral Imaging rely on Artificial Intelligence techniques, particularly Deep Neural Networks, which often outperform other algorithms. However, despite their effectiveness, these techniques are considered opaque for the non-linearity of the underlying model, making a conscious results interpretation difficult even for experts in the application field. The present work aims to describe the results of our experiments toward Explainable Artificial Intelligence techniques for hyperspectral remote sensing image classification in Edge Computing environments. The proposed technique extends the traditional 2D Gradient-weighted Class Activation Mapping (Grad-CAM) to 3D Convolutional Neural Networks. Moreover, we use spectral-cumulative Grad-CAM and class probability as complementary methods. The experimental results confirm that the observer can interpret the choices made within neural network layers by visualizing the activation volumes provided by the proposed method. Graphical abstract: Highlights: Image classification can benefit from the interaction between Edge Computing and Artificial Intelligence. Explainable Artificial Intelligence can improve the interpretability of the results achieved through Neural Networks. The proposed XAI method extends traditional 2D Grad-CAM to 3D CNNs. The results confirm that the observer can better understand the choices made within neural network layers.