.png){kind=icon}
返回
Hyperspectral Image Classification Using Principal Components-Based Smooth Ordering and Multiple 1-D Interpolation
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2016.2621058.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time.
Keyword:
Difficultly separable samples (DSSs)
easily separable samples (ESSs)
highly confident set
multiple 1-D interpolation
principal component (PC)-based smooth ordering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
引用论文
.png){kind=icon}
A Survey on Spectral-Spatial Classification Techniques Based on Attribute Profiles基于属性剖面的光谱-空间分类技术综述
PREAI
分享收藏
查看更多内容