A Spectral Sequence-Based Nonlocal Long Short-Term Memory Network for Hyperspectral Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Baoxiang Huang¹  Zhipu Wang¹  Jie Shang²  Milena Radenkovic³  Ge Chen⁴ 

[1] Department of Computer Science and Technology, Qingdao University, Qingdao, China;North China Sea Marine Forecasting Center, State Oceanic Administration, Qingdao, China;School of Computer Science and Information Technology, University of Nottingham, Nottingham, U.K.;School of Marine Technology and Institute for Advanced Ocean Study, Ocean University of China, Qingdao, China;

关键词: Attention mechanism;    hyperspectral image (HSI) classification;    long short-term memory (LSTM);    nonlocal diverse region;    spectral information;   

DOI  :  10.1109/JSTARS.2022.3159729

来源: DOAJ

【 摘 要 】

Efficient classification for hyperspectral image (HSI), which assigns each pixel of the image into a specific category, has been a critical research topic in the HSI analysis area. Under the supervised classification settings, the deep learning approaches are very useful for label prediction. However, most deep learning modeling methods cannot get the utmost out of spectral information, which is critically important for object interpretation. Consequently, a spectral sequence-based nonlocal long short-term memory (LSTM) network for HSI classification is proposed in this article. To boost the dominant role of spectral information, the LSTM network for sequential data processing is employed. Furthermore, the nonlocal diverse regions are exploited to learn contextual features for stronger discriminative ability. Meanwhile, the attention mechanism is adopted to increase the classification performance. Experiments on Salinas, Indian Pines, Pavia University, and Houston University datasets are implemented. Benefiting from the idea of processing spectral bands as sequence data and nonlocal diverse regions, spectral-spatial information is fully fused to achieve accurate pixel-level classification. It can be demonstrated that the proposed method can achieve better quantitative and qualitative classification results compared with the other state-of-the-art classification methods.

【 授权许可】

Unknown