A Probabilistic Method for the Classification of Hyperspectral Images

被引：0
|
作者：
Kutluk, Sezer [1 ]
Kayabol, Koray [2 ]
Akan, Aydin [1 ,3 ]
机构：
[1] Istanbul Univ, Elekt Elekt Muhendisligi Bolumu, Istanbul, Turkey
[2] Gebze Tekn Univ, Elekt Muhendisligi Bolumu, Kocaeli, Turkey
[3] Izmir Katip Celebi Univ, Biyomed Muhendisligi Bolumu, Izmir, Turkey
来源：
2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU) | 2016年
关键词：
hyperspectral image; probabilistic principal component analysis; dimensionality reduction; mixture models; MODEL; SEGMENTATION; MIXTURES;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this study a supervised classification and dimensionality reduction method for hyperspectral images is proposed. For this purpose, using probabilistic principal component analysis (PPCA), dimensionality reduction is performed and a Gaussian mixture model (GMM) is built. Alongside this mixture model, spatial information is also included into the classification process by taking advantage of pixel neighborhoods.
引用
收藏
页码：905 / 908
页数：4
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