高光谱成像
计算机科学
卷积神经网络
变压器
像素
模式识别(心理学)
人工智能
上下文图像分类
卷积码
预处理器
图像(数学)
算法
解码方法
量子力学
物理
电压
作者
Zhengang Zhao,Dan Hu,Hao Wang,Xianchuan Yu
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:19: 1-5
被引量:24
标识
DOI:10.1109/lgrs.2022.3169815
摘要
Convolutional neural networks (CNNs) have attained remarkable performance in hyperspectral image (HSI) classification. However, the existing CNNs are restricted by their limited receptive field in HSI classification. Recently, transformer networks have proved to be promising in many tasks thanks to the global receptive field, but they easily ignore some local information that is important for HSI classification. In this letter, we propose a novel method entitled convolutional transformer network (CTN) for HSI classification. In order to make full use of spectral information and spatial information, the method adopts center position encoding (CPE) to merge spectral features and pixel positions. Furthermore, the proposed method introduces convolutional transformer (CT) blocks. It effectively combines convolution and transformer structures together to capture local–global features of HSI patches, which is contributive for HSI classification. Experimental results on public datasets demonstrate the superiority of our method compared with several state-of-the-art classification methods. The codes of this work will be available at https://github.com/sky8791 to facilitate reproducibility.
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