A capsule network for hyperspectral image classification employing spatial-spectral feature

An efficient and stable deep learning classifier can improve the classification accuracy of hyperspectral remote sensing images. In order to deal with the insufficiency of the scalar neuron's limited feature expression ability and the inability to effectively model the spatial hierarchical relationship among features in convolutional neural networks, an end-to-end hyperspectral capsule network (H-CapsNet) was designed considering the characteristics of hyperspectral image. The main body of H-CapsNet is composed of encoder (Conv, PrimaryCaps and DigitCats) and decoder (fully connection layer). It mainly embeds channel and spatial attention modules at the network input to enhance the model's capture and recognition of spatial and spectral features, thereby improving the network's ability to focus and express features. Taking the hyperspectral images of Zhang-jia-gang city and two public datasets:University of Pavia and University of Houston, as examples, the performance of the proposed H-CapsNet was compared with traditional machine learning algorithms and several deep neural networks. The experimental results show that the H-CapsNet has achieved the best classification accuracy on three hyperspectral images with different resolutions, and the overall accuracy is improved by 2.36%~7.67%, 0.16%~11.8% and 1.75%~15.58% compared with other methods. In particular, the H-CapsNet has good adaptability to small pixel neighborhoods. When the image patch size is limited, it can still achieve relatively ideal classification results. © 2023 SinoMaps Press. All rights reserved.