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Two-Dimensional Spectral Representation
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AI
DOI：10.1109/TGRS.2023.3343909.png)
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Abstract
En 中文
In this article, a two-dimensional (2-D) spectral representation is proposed for the visualization and classification of hyperspectral images (HSIs). First, several sequence data processing methods, i.e., Gramian angular field (GAF) algorithm, Markov transition field (MTF), and recurrence plot (REP), are applied to obtain multiple 2-D features of a one-dimensional (1-D) spectrum. Second, the 2-D spectral features are stacked together to form the final 2-D spectral representation. Finally, many excellent classifiers in computer vision field are applied on the 2-D spectral representation to obtain the final classification result. Furthermore, 114 target spectral visualization maps are established based on their 1-D spectra. Experimental results reveal that the 2-D spectral representation has multiple advantages in terms of better visual quality and classification accuracies. The code of this work is available at https://github.com/zhuyongxiang1/two-dimensional-spectral-representation.
Keywords:
Hyperspectral imaging
Feature extraction
Deep learning
Markov processes
Computational modeling
Convolutional neural networks
Visualization
Hyperspectral image (HSI)
spectral representation
spectral visualization
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W