An End-to-End Automatic Classification Algorithm for Hyperspectral Images via 3D Convolution and Swin-Transformer

被引:0
作者
Guo, Ningbo [1 ]
Yang, Lei [2 ]
Yue, Cong [2 ]
Zhang, Honggang [1 ]
Jiang, Mingyong [1 ]
Li, Yinan [1 ]
机构
[1] Space Engn Univ, Sch Space Informat, Beijing, Peoples R China
[2] Acad Peoples Armed Police, Beijing, Peoples R China
来源
39TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION, YAC 2024 | 2024年
关键词
Hyperspectral; deep learning; Automatic classification;
D O I
10.1109/YAC63405.2024.10598808
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Deep Learning (DL) is one of the key technologies to realize artificial intelligence (AI), which achieves automatic classification and prediction of data by simulating the learning process of the human brain. Convolutional neural network (CNN) is a very important architecture in DL, which is being widely used in the classification task of hyperspectral images (HSI).The local convolutional operation in CNN cannot learn the global semantic information in HSI adequately, but the multi-attention mechanism in the Swin-Transformer can fully utilize the rich discriminative information. So we jointly design an "end-to-end" deep network model with Swin-Transformer to further improve the classification accuracy of HSI. First, the HSI is downscaled using principal component analysis, and the neighborhood data around the pixel is selected as the input samples to make full use of the null-spectrum joint information in the image; then, the input samples are converted into sequence feature vectors using convolutional layers; finally, the classification is carried out by using the constructed Swin-Transformer blocks. The experiments show that our method can achieve better classification performance than some existing CNN models.
引用
收藏
页码:1400 / 1405
页数:6
相关论文
共 15 条
[1]  
Agarap Abien Fred, 2019, arXiv, DOI [DOI 10.48550/ARXIV.1803.08375, 10.48550/arXiv.1803.08375]
[2]   A Fast and Compact 3-D CNN for Hyperspectral Image Classification [J].
Ahmad, Muhammad ;
Khan, Adil Mehmood ;
Mazzara, Manuel ;
Distefano, Salvatore ;
Ali, Mohsin ;
Sarfraz, Muhammad Shahzad .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
[3]  
[Anonymous], 2015, 2015 INT C COMMUNICA
[4]   3-D Deep Learning Approach for Remote Sensing Image Classification [J].
Ben Hamida, Amina ;
Benoit, Alexandre ;
Lambert, Patrick ;
Ben Amar, Chokri .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2018, 56 (08) :4420-4434
[5]   A Deep-Learned Embedding Technique for Categorical Features Encoding [J].
Dahouda, Mwamba Kasongo ;
Joe, Inwhee .
IEEE ACCESS, 2021, 9 :114381-114391
[6]  
Haridas Nikhila, Hyperspectral image classification using random kitchen sink and regularized least squares
[7]   Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges [J].
Jaiswal, Garima ;
Rani, Ritu ;
Mangotra, Harshita ;
Sharma, Arun .
COMPUTER SCIENCE REVIEW, 2023, 50
[8]   History of artificial intelligence in medicine [J].
Kaul, Vivek ;
Enslin, Sarah ;
Gross, Seth A. .
GASTROINTESTINAL ENDOSCOPY, 2020, 92 (04) :807-812
[9]   Imbalanced image classification with complement cross entropy [J].
Kim, Yechan ;
Lee, Younkwan ;
Jeon, Moongu .
PATTERN RECOGNITION LETTERS, 2021, 151 :33-40
[10]   A spectral-spatial kernel-based method for hyperspectral imagery classification [J].
Li, Li ;
Ge, Hongwei ;
Gao, Jianqiang .
ADVANCES IN SPACE RESEARCH, 2017, 59 (04) :954-967