Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks

doi:10.1109/JSTARS.2021.3099118

	Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks
	Ghaderizadeh, Saeed 2; Abbasi-Moghadam, Dariush 2; Sharifi, Alireza 1; Zhao, Na 4; Tariq, Aqil 3
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2021
卷号	14 页码:7570-7588
关键词	Feature extraction Three-dimensional displays Convolution Solid modeling Hyperspectral imaging Kernel Convolutional neural networks Convolutional neural network (CNN) deep learning hyperspectral image (HSI) classification spectral-spatial features
ISSN号	1939-1404
DOI	10.1109/JSTARS.2021.3099118
通讯作者	Sharifi, Alireza(a_sharifi@sru.ac.ir)
英文摘要	Due to the unique feature of the three-dimensional convolution neural network, it is used in image classification. There are some problems such as noise, lack of labeled samples, the tendency to overfitting, a lack of extraction of spectral and spatial features, which has challenged the classification. Among the mentioned problems, the lack of experimental samples is the main problem that has been used to solve the methods in recent years. Among them, convolutional neural network-based algorithms have been proposed as a popular option for hyperspectral image analysis due to their ability to extract useful features and high performance. The traditional convolutional neural network (CNN) based methods mainly use the two-dimensional CNN for feature extraction, which makes the interband correlations of HSIs underutilized. The 3-D-CNN extracts the joint spectral-spatial information representation, but it depends on a more complex model. To address these issues, the report uses a 3-D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2-D convolutional neural network was introduced to extract spectral-spatial features. Using a hybrid CNN reduces the complexity of the model compared to using 3-D-CNN alone and can also perform well against noise and a limited number of training samples. In addition, a series of optimization methods including batch normalization, dropout, exponential decay learning rate, and L2 regularization are adopted to alleviate the problem of overfitting and improve the classification results. To test the performance of this hybrid method, it is performed on the Salinas, University Pavia and Indian Pines datasets, and the results are compared with 2-D-CNN and 3-D-CNN deep learning models with the same number of layers.
资助项目	National Natural Science Foundation of China[42071374]
WOS关键词	REMOTE-SENSING IMAGES ; DISTANCE
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000684698600003
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/164605]
专题	中国科学院地理科学与资源研究所
通讯作者	Sharifi, Alireza
作者单位	1.Shahid Rajaee Teacher Training Univ, Fac Civil Engn, Surveying Engn, Tehran 1678815811, Iran 2.Shahid Bahonar Univ Kerman, Dept Elect Engn, Kerman 7616914111, Iran 3.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Ghaderizadeh, Saeed,Abbasi-Moghadam, Dariush,Sharifi, Alireza,et al. Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:7570-7588.
APA	Ghaderizadeh, Saeed,Abbasi-Moghadam, Dariush,Sharifi, Alireza,Zhao, Na,&Tariq, Aqil.(2021).Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,7570-7588.
MLA	Ghaderizadeh, Saeed,et al."Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):7570-7588.