LAUSR

Convolutional neural network (CNN) has shown its powerful ability for hyperspectral image (HSI) classification, which however, is difficult to deploy on resource-limited or low-latency platforms due to its parameter and computation redundancy. Though binary neural network (BNN) has attracted attention for its extreme compressing and speeding up ability by binarizing both weights and activations, it has rarely been explored for HSI classification. In this study, we elaborately design a BNN with good performance for HSI classification task. Specifically, an adaptive gradient scale module is proposed to flexibly modify the gradient during training stage to better optimize the BNN and does not add any extra computation for inference. Furthermore, a curriculum learning-based progressive binarization strategy is utilized to improve the performance. Compared with the existing BNN works, our method can increase the HSI classification accuracy by a large margin while maintaining the compressing ratio. Abundant experiments on three datasets demonstrate the effectiveness of the proposed method.