Center attention network for hyperspectral image classification

Abstract

Classification is one of the most important research topics in hyperspectral image (HSI) analyses and applications. Although convolutional neural networks (CNNs) have been widely introduced into the study of HSI classification with appreciable performance, the misclassification problem of the pixels on the boundary of adjacent land covers is still significant due to the interfering neighboring pixels whose categories are different from the target pixel. To address this challenge, in this article, we propose a center attention network for HSI classification. The proposed method simultaneously captures spectral-spatial features of the target pixel and its neighboring pixels for classification. Specifically, the method adopts a center attention module (CAM) that pays more attention to the features which are more correlated with the target pixel, that is, the central pixel of the sample, and then sums up the weighted features to generate more relevant and discriminative features. In this way, our method has a high potential for improving the performance of HSI classification. In addition, the CAM greatly reduces the number of parameters in the network via weighted sum of the spectral-spatial features, thus improving the computing efficiency while still maintaining classification accuracy. We evaluate the proposed method on three public datasets, and the experimental results demonstrate the superiority of our method on accuracy and efficiency compared with several state-of-the-art methods.