Comparer des méthodes

	Classification d'images satellitaires spatio-temporelles	Krigage spatio-temporel
Domaine	Analyse spatiale	Analyse spatiale
Famille	Regression model	Regression model
Année d'origine	1980s-2000s	1999
Auteur d'origine	Woodcock, Zhu, and remote sensing community	Cressie & Huang; Kyriakidis & Journel
Type	Multi-temporal image classification	Geostatistical interpolation
Source fondatrice	Zhu, Z. (2017). Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 370-384.	Cressie, N., & Huang, H.-C. (1999). Classes of nonseparable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94(448), 1330-1340.
Alias	multi-temporal remote sensing classification, spatio-temporal image classification, temporal remote sensing analysis, STRSC	spatiotemporal kriging, ST-kriging, space-time geostatistical interpolation, kriging in space-time
Apparentées	4	4
Résumé	Space-Time Remote Sensing Classification extends standard image classification to multi-temporal satellite or aerial imagery, enabling analysts to track land cover change, phenological cycles, and environmental dynamics across both space and time. By incorporating the temporal dimension, classifiers achieve higher accuracy and can detect transitions that a single-date analysis would miss.	Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear weights to produce predictions with quantified uncertainty.