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- W2980930055 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." @default.
- W2980930055 created "2019-10-25" @default.
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- W2980930055 date "2020-08-01" @default.
- W2980930055 modified "2023-10-18" @default.
- W2980930055 title "Combining t-Distributed Stochastic Neighbor Embedding With Convolutional Neural Networks for Hyperspectral Image Classification" @default.
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- W2980930055 doi "https://doi.org/10.1109/lgrs.2019.2945122" @default.
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