D<sup>2</sup>S<sup>2</sup>BoT: Dual-Dimension Spectral-Spatial Bottleneck Transformer for Hyperspectral Image Classification

D<sup>2</sup>S<sup>2</sup>BoT: Dual-Dimension Spectral-Spatial Bottleneck Transformer for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has become a popular research topic in recent years, and transformer-based networks have demonstrated superior performance by analyzing global semantic features. However, using transformers for pixel-level HSI classification has two limitations: ineffective c...

Full description

Bibliographic Details
Main Authors: Lan Zhang, Yang Wang, Linzi Yang, Jianfeng Chen, Zijie Liu, Lifeng Bian, Chen Yang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
dual-dimension self-attention (D<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$_2$</tex-math> </inline-formula> </named-content>SA) mechanism
hyperspectral image (HSI) classification
remote sensing
transformer
Online Access:https://ieeexplore.ieee.org/document/10360438/

Internet

https://ieeexplore.ieee.org/document/10360438/

Similar Items