Fast Object-Level Change Detection for VHR Images
文献类型:期刊论文
作者 | Huo, Chunlei1![]() ![]() ![]() |
刊名 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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出版日期 | 2010 |
卷号 | 7期号:1页码:118-122 |
关键词 | Fast multitemporal segmentation object-level change vector analysis progressive classification |
英文摘要 | Anovel approach is presented for change detection of very high resolution images, which is accomplished by fast object-level change feature extraction and progressive change feature classification. Object-level change feature is helpful for improving the discriminability between the changed class and the unchanged class. Progressive change feature classification helps improve the accuracy and the degree of automation, which is implemented by dynamically adjusting the training samples and gradually tuning the separating hyperplane. Experiments demonstrate the effectiveness of the proposed approach. |
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] | SEGMENTATION ; DOMAIN ; SVM |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000276079000024 |
源URL | [http://ir.ia.ac.cn/handle/173211/3687] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Beijing Inst Remote Sensing, Beijing 100854, Peoples R China |
推荐引用方式 GB/T 7714 | Huo, Chunlei,Zhou, Zhixin,Lu, Hanqing,et al. Fast Object-Level Change Detection for VHR Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2010,7(1):118-122. |
APA | Huo, Chunlei,Zhou, Zhixin,Lu, Hanqing,Pan, Chunhong,&Chen, Keming.(2010).Fast Object-Level Change Detection for VHR Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,7(1),118-122. |
MLA | Huo, Chunlei,et al."Fast Object-Level Change Detection for VHR Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 7.1(2010):118-122. |
入库方式: OAI收割
来源:自动化研究所
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