Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

Alexandre Constantin; Mathieu Fauvel; Stéphane Girard

首页> 外文期刊>Statistics and computing >Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

【24h】

Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

机译：Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相关主题

摘要

Abstract The classification of irregularly sampled Satellite image time-series (SITS) is investigated in this paper. A multivariate Gaussian process mixture model is proposed to address the irregular sampling, the multivariate nature of the time-series and the scalability to large data-sets. The spectral and temporal correlation is handled using a Kronecker structure on the covariance operator of the Gaussian process. The multivariate Gaussian process mixture model allows both for the classification of time-series and the imputation of missing values. Experimental results on simulated and real SITS data illustrate the importance of taking into account the spectral correlation to ensure a good behavior in terms of classification accuracy and reconstruction errors.

著录项

来源
《Statistics and computing》 |2022年第5期|79.1-79.20|共20页
作者
Alexandre Constantin; Mathieu Fauvel; Stéphane Girard;
展开▼
作者单位

Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK;

Université de Toulouse, CNES/CNRS/INRAe/IRD/UPS;

展开▼
收录信息
原文格式 PDF
正文语种英语
中图分类
关键词
Multivariate Gaussian processes; Classification; Multivariate imputation of missing data; Irregular sampling; Satellite image time-series (SITS); Remote sensing;

Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

摘要

著录项

相关主题

期刊订阅