Heisenberg principle-inspired filters size setting in 3D CNN for hyperspectral data classification

The paper presents an empirical study concerning filters size adjustment in 3D Convolutional Neural Networks (CNN) for hyperspectral data classification. Based on Heisenberg’s uncertainty principle, constraints on the depth-dependent relationships between spatial and spectral filter sizes have been defined. The benefits of CNNs satisfying these constraints have been evaluated for raw and transformed input data, in terms of optimization of both classification accuracy and training time. Experimental results show that the filter setting based on Heisenberg principle provides a significant reduction in training time and high classification accuracy. In particular, it can be a viable and feasible alternative to depth reduction in the case of transformed input data.