Combining t-Distributed Stochastic Neighbor Embedding with Convolutional Neural Networks for Hyperspectral Image Classification

Lianru Gao, Daixin Gu, Lina ZHUANG, Jinchang Ren, Dong Yang, Bing Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Article number8876672
Pages (from-to)1368-1372
Number of pages5
JournalIEEE Geoscience and Remote Sensing Letters
Volume17
Issue number8
DOIs
Publication statusPublished - Aug 2020

Scopus Subject Areas

  • Geotechnical Engineering and Engineering Geology
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Assembly fusion
  • convolutional neural network (CNN)
  • dimensionality reduction
  • hyperspectral image (HSI) classification
  • t-distributed stochastic neighbor embedding (t-SNE)

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