Hierarchical Remote Sensing Image Analysis via Graph Laplacian Energy
文献类型：期刊论文
| 作者 | Zhang Huigang1; Bai Xiao1; Zheng Huaxin2; Zhao Huijie2; Zhou Jun3; Cheng Jian4 ; Lu Hanqing4
|
| 刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
 |
| 出版日期 | 2013-03-01 |
| 卷号 | 10期号:2页码:396-400 |
| 关键词 | Classification graph Laplacian energy (LE) high-resolution imagery local self-similarity (LSS) |
| 英文摘要 | Segmentation and classification are important tasks in remote sensing image analysis. Recent research shows that images can be described in hierarchical structure or regions. Such hierarchies can produce the state-of-the-art segmentations and can be used in the classification. However, they often contain more levels and regions than required for an efficient image description, which may cause increased computational complexity. In this letter, we propose a new hierarchical segmentation method that applies graph Laplacian energy as a generic measure for segmentation. It reduces the redundancy in the hierarchy by an order of magnitude with little or no loss of performance. In the classification stage, we apply local self-similarity feature to capture the internal geometric layouts of regions in an image. By incorporating advantages from both semantic hierarchical segmentation and local geometric region description, we have achieved better performance than those from the methods being compared. In the experimental section, we validate the effectiveness of our method by showing results on QuickBird and GeoEye-1 image data sets. |
| WOS标题词 | Science & Technology ; Physical Sciences ; Technology |
| 类目[WOS] | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
| 研究领域[WOS] | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 关键词[WOS] | CLASSIFICATION ; SEGMENTATION ; FEATURES |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000310901600039 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3338]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 作者单位 | 1.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China 2.Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100191, Peoples R China 3.Griffith Univ, Sch Informat & Commun Technol, Nathan, Qld 4111, Australia 4.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang Huigang,Bai Xiao,Zheng Huaxin,et al. Hierarchical Remote Sensing Image Analysis via Graph Laplacian Energy[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2013,10(2):396-400. |
| APA | Zhang Huigang.,Bai Xiao.,Zheng Huaxin.,Zhao Huijie.,Zhou Jun.,...&Lu Hanqing.(2013).Hierarchical Remote Sensing Image Analysis via Graph Laplacian Energy.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,10(2),396-400. |
| MLA | Zhang Huigang,et al."Hierarchical Remote Sensing Image Analysis via Graph Laplacian Energy".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 10.2(2013):396-400. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。