A Supervised Segmentation Network for Hyperspectral Image Classification | |
Sun, Hao1,2; Zheng, Xiangtao3; Lu, Xiaoqiang3 | |
作者部门 | 光谱成像技术研究室 |
2021 | |
发表期刊 | IEEE Transactions on Image Processing |
ISSN | 10577149;19410042 |
卷号 | 30页码:2810-2825 |
产权排序 | 1 |
摘要 | Recently, deep learning has drawn broad attention in the hyperspectral image (HSI) classification task. Many works have focused on elaborately designing various spectral-spatial networks, where convolutional neural network (CNN) is one of the most popular structures. To explore the spatial information for HSI classification, pixels with its adjacent pixels are usually directly cropped from hyperspectral data to form HSI cubes in CNN-based methods. However, the spatial land-cover distributions of cropped HSI cubes are usually complicated. The land-cover label of a cropped HSI cube cannot simply be determined by its center pixel. In addition, the spatial land-cover distribution of a cropped HSI cube is fixed and has less diversity. For CNN-based methods, training with cropped HSI cubes will result in poor generalization to the changes of spatial land-cover distributions. In this paper, an end-to-end fully convolutional segmentation network (FCSN) is proposed to simultaneously identify land-cover labels of all pixels in a HSI cube. First, several experiments are conducted to demonstrate that recent CNN-based methods show the weak generalization capabilities. Second, a fine label style is proposed to label all pixels of HSI cubes to provide detailed spatial land-cover distributions of HSI cubes. Third, a HSI cube generation method is proposed to generate plentiful HSI cubes with fine labels to improve the diversity of spatial land-cover distributions. Finally, a FCSN is proposed to explore spectral-spatial features from finely labeled HSI cubes for HSI classification. Experimental results show that FCSN has the superior generalization capability to the changes of spatial land-cover distributions. © 1992-2012 IEEE. |
关键词 | Hyperspectral imaging Feature extraction Training Task analysis Imaging Image segmentation Testing Hyperspectral image (HSI) classification fully convolutional segmentation network (FCSN) generalization |
DOI | 10.1109/TIP.2021.3055613 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000617758400007 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20210809947554 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/94508 |
专题 | 光谱成像技术研究室 |
通讯作者 | Zheng, Xiangtao |
作者单位 | 1.Key Laboratory of Spectral Imaging Technology Cas, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; 2.The University of Chinese Academy of Sciences, Beijing; 100049, China; 3.Key Laboratory of Spectral Imaging Technology Cas, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China |
推荐引用方式 GB/T 7714 | Sun, Hao,Zheng, Xiangtao,Lu, Xiaoqiang. A Supervised Segmentation Network for Hyperspectral Image Classification[J]. IEEE Transactions on Image Processing,2021,30:2810-2825. |
APA | Sun, Hao,Zheng, Xiangtao,&Lu, Xiaoqiang.(2021).A Supervised Segmentation Network for Hyperspectral Image Classification.IEEE Transactions on Image Processing,30,2810-2825. |
MLA | Sun, Hao,et al."A Supervised Segmentation Network for Hyperspectral Image Classification".IEEE Transactions on Image Processing 30(2021):2810-2825. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A Supervised Segment(4384KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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