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Previous studies have shown the great potential of capsule networks for thespatial contextual feature extraction from {hyperspectral images (HSIs)}.However, the sampling locations of the convolutional kernels of capsules arefixed and cannot be adaptively changed according to the inconsistent semanticinformation of HSIs. Based on this observation, this paper proposes an adaptivespatial pattern capsule network (ASPCNet) architecture by developing anadaptive spatial pattern (ASP) unit, that can rotate the sampling location ofconvolutional kernels on the basis of an enlarged receptive field. Note thatthis unit can learn more discriminative representations of HSIs with fewerparameters. Specifically, two cascaded ASP-based convolution operations(ASPConvs) are applied to input images to learn relatively high-level semanticfeatures, transmitting hierarchical structures among capsules more accuratelythan the use of the most fundamental features. Furthermore, the semanticfeatures are fed into ASP-based conv-capsule operations (ASPCaps) to explorethe shapes of objects among the capsules in an adaptive manner, furtherexploring the potential of capsule networks. Finally, the class labels of imagepatches centered on test samples can be determined according to the fullyconnected capsule layer. Experiments on three public datasets demonstrate thatASPCNet can yield competitive performance with higher accuracies thanstate-of-the-art methods.

ASPCNet: A Deep Adaptive Spatial Pattern Capsule Network for Hyperspectral Image Classification