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Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification

16 Jul 2022Remote Sensing 2022 7archive 2025-07-28

Jing Bai, Jiawei Lu, Zhu Xiao, Zheng Chen, Licheng Jiao

Nowadays, HSI classification can reach a high classification accuracy when given sufficient labeled samples as training set. However, the performances of existing methods decrease sharply when trained on few labeled samples. Existing methods in few-shot problems usually require another dataset in order to improve the classification accuracy. However, the cross-domain problem exists in these methods because of the significant spectral shift between target domain and source domain. Considering above issues, we propose a new method without requiring external dataset through combining a Generative Adversarial Network, Transformer Encoder and convolution block in a unified framework. The proposed method has both a global receptive field provided by Transformer Encoder and a local receptive field provided by convolution block. Experiments conducted on Indian Pines, PaviaU and KSC datasets demonstrate that our method exceeds the results of existing deep learning methods for hyperspectral image classification in the few-shot learning problem.

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Tasks

Classification Classification Of Hyperspectral Images Few-Shot Image Classification Few-Shot Learning Hyperspectral Image Classification Image Classification image-classification

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Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hyperspectral Image Classification	Indian Pines	TC-GAN	OA@15perclass	87.47±1.45	#4 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Kennedy Space Center	TC-GAN	OA@15perclass	98.39±0.63	#2 of 14	Archive leaderboard	report
Hyperspectral Image Classification	Pavia University	TC-GAN	OA@15perclass	93.20±0.59	#2 of 33	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position Encodings Adam Attention BPE Convolution Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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