Salīdzināt metodes
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| Attēlu klasifikācija ar attālināto uztveršanu× | Karstā punkta analīze (Getis-Ord Gi*)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1970s–present | 1992 |
| Autors≠ | Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments) | Arthur Getis and J. Keith Ord |
| Tips≠ | Supervised / unsupervised image classification | Local spatial statistic |
| Pirmavots≠ | Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley. ISBN: 978-1118343289 | Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189-206. DOI ↗ |
| Citi nosaukumi | land cover classification, image classification, satellite image classification, spectral classification | Getis-Ord Gi* statistic, spatial hot spot detection, cluster and outlier analysis, HSA |
| Saistītās≠ | 4 | 5 |
| Kopsavilkums≠ | Remote sensing classification assigns discrete thematic labels — such as forest, urban, water, or cropland — to pixels in a satellite or aerial image based on their spectral, spatial, and temporal properties. It underpins land-use/land-cover mapping, change detection, environmental monitoring, and disaster response at local to global scales. | 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. |
| ScholarGateDatu kopa ↗ |
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