  Previous studies have shown the great potential of capsule networks for the
spatial contextual feature extraction from {hyperspectral images (HSIs)}.
However, the sampling locations of the convolutional kernels of capsules are
fixed and cannot be adaptively changed according to the inconsistent semantic
information of HSIs. Based on this observation, this paper proposes an adaptive
spatial pattern capsule network (ASPCNet) architecture by developing an
adaptive spatial pattern (ASP) unit, that can rotate the sampling location of
convolutional kernels on the basis of an enlarged receptive field. Note that
this unit can learn more discriminative representations of HSIs with fewer
parameters. Specifically, two cascaded ASP-based convolution operations
(ASPConvs) are applied to input images to learn relatively high-level semantic
features, transmitting hierarchical structures among capsules more accurately
than the use of the most fundamental features. Furthermore, the semantic
features are fed into ASP-based conv-capsule operations (ASPCaps) to explore
the shapes of objects among the capsules in an adaptive manner, further
exploring the potential of capsule networks. Finally, the class labels of image
patches centered on test samples can be determined according to the fully
connected capsule layer. Experiments on three public datasets demonstrate that
ASPCNet can yield competitive performance with higher accuracies than
state-of-the-art methods.
