高光谱成像
计算机科学
人工智能
模式识别(心理学)
领域(数学分析)
变压器
上下文图像分类
图像(数学)
机器学习
数学
工程类
电压
电气工程
数学分析
作者
Jiawei Ling,Minchao Ye,Yuntao Qian,Qipeng Qian
标识
DOI:10.1109/igarss52108.2023.10283183
摘要
Small-sample-size problem is a big challenge in hyperspectral image (HSI) classification. Deep learning-based methods, especially Transformer, may need more training samples to train a satisfactory model. Cross-domain classification has been proven to be effective in handling the small-sample-size problem. In two HSI scenes sharing the same land-cover classes, one with sufficient labeled samples is called the source domain, while the other with limited labeled samples is called the target domain. Thus, the information on the source domain could help the target domain improve classification performance. This paper proposes a cross-domain Vision Transformer (CD-ViT) method for heterogeneous HSI classification. CD-ViT maps the source samples to the target domain for supplementing training samples. In addition, cross-attention is used to align the source and target features. Moreover, knowledge distillation is employed to learn more transferable information. Experiments on three different cross-domain HSI datasets demonstrate the effectiveness of the proposed approach.
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