.png){kind=icon}
返回
Adaptive Classification for Hyperspectral Image Data Using Manifold Regularization Kernel Machines
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2010.2076287.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Localized training data typically utilized to develop a classifier may not be fully representative of class signatures over large areas but could potentially provide useful information which can be updated to reflect local conditions in other areas. An adaptive classification framework is proposed for this purpose, whereby a kernel machine is first trained with labeled data and then iteratively adapted to new data using manifold regularization. Assuming that no class labels are available for the data for which spectral drift may have occurred, resemblance associated with the clustering condition on the data manifold is used to bridge the change in spectra between the two data sets. Experiments are conducted using spatially disjoint data in EO-1 Hyperion images, and the results of the proposed framework are compared to semisupervised kernel machines.
Keyword:
Adaptive classifier
hyperspectral
kernel machine
knowledge transfer
manifold regularization
期刊
IF:
8.6
论文数:
2.1W
被引数:
10.7W
机构
引用论文
.png){kind=icon}
Tinnitus Retraining Therapy (TRT) as a Method for Treatment of Tinnitus and Hyperacusis Patients耳鸣再训练疗法 (TRT) 作为治疗耳鸣和高亢患者的方法
PREAI
分享收藏Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
PREAI
分享收藏
查看更多内容