Adaptive pixel attention network for hyperspectral image classification

Patch features obtained by fixed convolution kernel have become the main form in hyperspectral image (HSI) classification processing. However, the fixed convolution kernel limits the weight learning of channels, which results in the potential connections between pixels not being captured in patches, and seriously affects the classification performance. To tackle the above issues, we propose a novel Adaptive Pixel Attention Network, which can improve HSI classification by further mining the connections between pixels in patch features. Specifically, a Spectral-Spatial Superposition Enhancement module is first proposed for enhancing the spectral-spatial information of 3D input cubes via complementing the 1D spectral vectors by zero and reflection filling operations. More importantly, we also propose a new Adaptive Pixel Attention mechanism, which explores Cosine and Euclidean similarity to adaptively explore the distance and angle relationship between pixels of different scale convolution patch features. Moreover, the Cross-Layer Information Complement module is designed to form a contextual interaction by integrating the output features of different convolution layers, which can prevent the omission of discriminative information and further improve the network performance. Experimental results on four widely used HSI datasets IP, UP, HU, and KSC show that the proposed network is superior to other state-of-the-art classification models in accuracy, and it also has a better efficiency than other 3D works.