Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification.

Gao, Lianru; Gu, Daixin; Zhuang, Lina; Ren, Jinchang; Yang, Dong; Zhang, Bing

Abstract

Hyperspectral images (HSIs), featured by high spectral resolution over a wide range of electromagnetic spectra, have been widely used to characterize materials with subtle differences in the spectral domain. However, a large number of bands and an insufficient number of sample pixels for each class are challenging for traditional machine learning-based classifiers. As alternative tools for feature extraction, neural networks have received extensive attention. This letter proposes to combine t-distributed stochastic neighbor embedding (t-SNE) with a convolutional neural network (CNN) for HSI classification. Our framework is designed to automatically capture the potential assembly features, which are extracted from both the dimension-reduced CNN (DR-CNN) and the multiscale-CNN. Experimental results show that the proposed classification framework outperforms several state-of-the-art techniques for three real data sets.

Citation

GAO, L., GU, D., ZHUANG, L., REN, J., YANG, D. and ZHANG, B. 2020. Combining t-distributed stochastic neighbor embedding with convolutional neural networks for hyperspectral image classification. IEEE geoscience and remote sensing letters [online], 17(8), pages 1368-1372. Available from: https://doi.org/10.1109/LGRS.2019.2945122

• Journal Article Type: Article
• Acceptance Date: Sep 23, 2019
• Online Publication Date: Oct 18, 2019
• Publication Date: Aug 31, 2020
• Deposit Date: Jun 30, 2022
• Publicly Available Date: Jun 30, 2022
• Journal: IEEE Geoscience and Remote Sensing Letters
• Print ISSN: 1545-598X
• Electronic ISSN: 1558-0571
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 8
• Pages: 1368-1372
• DOI: https://doi.org/10.1109/LGRS.2019.2945122
• Keywords: Assembly fusion; Convolutional neural network (CNN); Dimensionality reduction; Hyperspectral image (HSI) classification; T-distributed stochastic neighbor embedding (t-SNE)
• Public URL: https://rgu-repository.worktribe.com/output/1085560