Hyperspectral Image Classification Using a Hybrid 3D-2D Convolutional Neural Networks
文献类型：期刊论文
| 作者 | Ghaderizadeh, Saeed1; Abbasi-Moghadam, Dariush1; Sharifi, Alireza2; Zhao, Na3; Tariq, Aqil4 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 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. |
| WOS关键词 | REMOTE-SENSING IMAGES ; DISTANCE |
| 资助项目 | National Natural Science Foundation of China[42071374] |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000684698600003 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 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 Bahonar Univ Kerman, Dept Elect Engn, Kerman 7616914111, Iran 2.Shahid Rajaee Teacher Training Univ, Fac Civil Engn, Surveying Engn, Tehran 1678815811, Iran 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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