Coarse-Refined Local Attention Network for Hyperspectral Image Classification

Abstract

Recently, deep learning methods using the attention mechanism have generated considerable research interest for hyperspectral image classification. In many existing attention-based methods, global pooling is widely used to obtaining the attention weights. In general, there are multiple categories in a hyperspectral image, so the operation of global pooling is too crude and inappropriate. To alleviate this problem, we propose a coarse-refined local attention network (CRLAN) for hyperspectral image classification. CRLAN is composed of two stages of fully convolutional networks. The first stage employs a coarse local attention fully convolutional network for hyperspectral image classification. In this stage, local parameters are roughly estimated according to the original size of the hyperspectral image. In the second stage, the prediction classification probability of the first stage network is applied to obtain the refined local attention features. Finally, for testing convenience, these two stages are integrated into an end-to-end network. Experimental results on two public data sets demonstrate that CRLAN is effective in improving classification performance.