End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification

End-to-End Convolutional Network and Spectral-Spatial Transformer Architecture for Hyperspectral Image Classification

Although convolutional neural networks (CNNs) have proven successful for hyperspectral image classification (HSIC), it is difficult to characterize the global dependencies between HSI pixels at long-distance ranges and spectral bands due to their limited receptive domain. The transformer can compens...

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Bibliographic Details
Main Authors: Shiping Li, Lianhui Liang, Shaoquan Zhang, Ying Zhang, Antonio Plaza, Xuehua Wang
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
convolutional neural networks (CNNs)
hyperspectral image classification (HSIC)
spectral–spatial transformer
multi-head self-attention (MHSA)
Online Access:https://www.mdpi.com/2072-4292/16/2/325

Internet

https://www.mdpi.com/2072-4292/16/2/325

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