A Dual Frequency Transformer Network for Hyperspectral Image Classification

Abstract

Hyperspectral images (HSIs) provide detailed spectral information of objects to be detected and play an important role in distinguishing targets with a similar appearance. However, the characteristics of high dimensionality and complexity impose significant challenges for realizing pixelwise classification. Although existing convolutional neural networks and transformer-based models have presented promising performance for HSIs classification, they mainly extract features from spectral–spatial perspective and do not fully consider the information in the frequency domain. To address this issue, in this article, we reconsider feature extraction and HSIs classification from the frequency domain. Specifically, inspired by the observation that high-frequency information contains detailed features within a local receptive field, whereas low-frequency information provides global smooth variations, a frequency domain feature extraction (FDFE) block with dual branches is developed. In the FDFE block, a multihead neighborhood attention block and a global filter block are designed to capture high- and low-frequency features, respectively. Besides, a pixel embedding module is constructed. Based on these, a novel hierarchical dual frequency transformer network is developed. Extensive experiments are performed on three open public hyperspectral datasets to evaluate the performance of our developed method. The experimental results demonstrate that our method is efficient and robust for HSIs classification, achieving overall accuracies of 94.14%, 86.92%, and 96.72% on the University of Pavia, University of Houston, and University of Trento datasets, respectively.al