Detection of surface material based on hyperspectral imaging (HSI) analysis is an important and challenging task in remote sensing.It is widely known that spectral-spatial data exploitation performs better than traditional spectral pixel-wise procedures.Nowadays, convolutional neural networks (CNNs) have shown to be a powerful deep learning (DL) technique due their strong feature extraction ability.CNNs not only combine spectral-spatial information in lg gbb72mbubn a natural way, but have also shown to be able to learn translation-equivariant representations, i.e.
a translation of input features into an equivalent internal CNN feature map.This provides great robustness to spatial feature locations.However, as far as we know, CNNs do not exhibit a natural way to exploit rotation equivariance, i.e.make use of the fact that data patches in a HSI data cube are observed in different orientations due to their orientation or on the varying paths/orbits of the airborne/spaceborne spectrometers.
This article presents a rotation-equivariant CNN2D model for HSI wall-e bearbrick analysis, where traditional convolution kernels have been replaced by circular harmonic filters (CHFs).The obtained results over three well-known HSI datasets showcase the potential of the approach.