.png){kind=icon}
返回
Texture feature analysis using a Gauss-Markov model hyperspectral image classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2004.830170.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Texture analysis has been widely investigated in the monospectral and multispectral imagery domains. At the same time, new image sensors with a large number of bands (more than ten) have been designed. They are able to provide images with both fine spectral and spatial sampling, and are called hyperspectral images. The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we propose a probabilistic vector texture model, using a Gauss-Markov random field (MRF). The MRF parameters allow the characterization of different hyperspectral textures. A possible application of this work is the classification of urban areas. These areas are not well characterized by radiometry alone, and so we use the MRF parameters as new features in a maximum-likelihood classification algorithm. The results obtained on Airborne Visible/Infrared Imaging Spectrometer hyperspectral images demonstrate that a better classification is achieved when texture information is included in the analysis.
Keyword:
classification
hyperspectral image processing
Markov random fields (MRFs)
texture features
期刊
IF:
8.6
论文数:
2.2W
被引数:
10.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
查看更多内容