.png){kind=icon}
返回
Automatic relative radiometric normalization using iteratively weighted least square regression
.png){kind=icon}
PRE
AI
DOI：10.1080/01431160701271990.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Relative radiometric normalization among multiple remotely sensed images is an important step of preprocessing for applications such as change detection and image mosaicking. In this paper we present a new automatic normalization approach that uses the iteratively weighted least square regression technique. This approach does not require selection of the pseudo-invariant features beforehand as in some other traditional methods, and is robust to outliers since it adaptively places different weights on different pixels according to their probabilities of no-change. This approach is mainly applicable to cases where primary spectral differences between the two images are caused by variations in imaging conditions rather than phenological cycle or land cover changes. The effectiveness of this approach was demonstrated by two experiments using both artificially constructed data and remotely sensed images respectively. The experimental result seems promising and our approach shows accuracy comparable to normalization methods.
Keyword:
SATELLITE IMAGES
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:
2.6
论文数:
1.2W
被引数:
2.7W
机构
引用论文
.png){kind=icon}
RADIOMETRIC RECTIFICATION - TOWARD A COMMON RADIOMETRIC RESPONSE AMONG MULTIDATE, MULTISENSOR IMAGES
PREAI
分享收藏
没有更多内容
没有更多内容