AI-Net: Attention inception neural networks for hyperspectral image classification

Recently, deep learning methods have dominated many fileds thanks to its powerful discriminative feature learning ability. While for hyperspectral images (HSI) analysis, these deep neural networks methods suffer from overfitting as the number of labeled training samples are limited. Thus more efficient neural network architecture should be designed to improve the performance of HSI classification task. In this paper, a novel attention inception module is introduced to extract features dynamically from multi-resolution convolutional filters. The AI-NET constructed by stacking the proposed attention inception module can adaptively learn the network architecture by dynamically routing between the attention inception modules. By exploiting different spatial size convolutional filters and dynamic CNN architecture, more representative feature can be learned with limited training samples. Extensive experimental results have shown that the proposed method can adaptively adjust the network architecture and obtain better classification performance.