Hyperspectral image classification by integrating attention-based LSTM and hybrid spectral networks. Issue 9 (3rd May 2022)

• Record Type: Journal Article
• Authors: Ahmed AL-Kubaisi, Mohammed; Shafri, Helmi Zulhaidi Mohd; Ismail, Mohd Hasmadi; Yusof, Mohd Johari Mohd; Hashim, Shaiful Jahari Bin
• Is Part Of: International journal of remote sensing. Volume 43:Issue 9(2022)
• Page Start: 3450
• Page End: 3469
• Publication Date: 2022-05-03
• Subjects: Convolutional neural network -- hyperspectral image classification -- LSTM autoencoder -- spectral-spatial attention; Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2022.2093621
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduction technique that employs spectral attention and Hybrid Spectral Networks (HybridSN) with spatial attention for hyperspectral image classification. The goal of this approach is to improve the ability to classify hyperspectral images by increasing the capabilities of spectral-spatial feature fusion. Experiments on three hyperspectral datasets (Indian Pines, University of Pavia, and Houston University) demonstrate that our method's overall accuracy is 99.66%, 99.97%, and 99.17% under 20% of the training samples, respectively, which is superior to several well-known approaches.