Salīdzināt metodes

	Klasifikācija attēlu datos, izmantojot telpisko un laika informāciju	Karstā punkta analīze (Getis-Ord Gi*)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	1980s-2000s	1992
Autors	Woodcock, Zhu, and remote sensing community	Arthur Getis and J. Keith Ord
Tips	Multi-temporal image classification	Local spatial statistic
Pirmavots	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.	Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206.
Citi nosaukumi	multi-temporal remote sensing classification, spatio-temporal image classification, temporal remote sensing analysis, STRSC	Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA
Saistītās	4	5
Kopsavilkums	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.	Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold spots against a background of random variation.