.png){kind=icon}
返回
Hyperspectral image compression using JPEG2000 and principal component analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2006.888109.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Principal component analysis (PCA) is deployed in JPEG2000 to provide spectral decorrelation as well as spectral dimensionality reduction. The proposed scheme is evaluated in terms of rate-distortion performance as well as in terms of information preservation in an anomaly-detection task. Additionally, the proposed scheme is compared to the common approach of JPEG2000 coupled with a wavelet transform for spectral decorrelation. Experimental results reveal that, not only does the proposed PCA-based coder yield rate-distortion and information-preservation performance superior to that of the wavelet-based coder, the best PCA performance occurs when a reduced number of PCs are retained and coded. A linear model to estimate the optimal number of PCs to use in such dimensionality reduction is proposed.
Keyword:
hyperspectral data compression
JPEG2000
principal component analysis (PCA)
wavelet transforms
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:
16.4
论文数:
1.0W
被引数:
5.1K
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏Traffic Light Detection using Convolutional Neural Networks and Lidar Data基于卷积神经网络和激光雷达数据的交通信号灯检测
PREAI
分享收藏 没有更多内容
没有更多内容