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| Attēlu klasifikācija ar attālināto uztveršanu× | Daudzskalu ģeogrāfiski svērtā regresija (MGWR)× | |
|---|---|---|
| Nozare | Telpiskā analīze | Telpiskā analīze |
| Saime | Regression model | Regression model |
| Izcelsmes gads≠ | 1970s–present | 2017 |
| Autors≠ | Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments) | A. Stewart Fotheringham, Wei Yang, and Wei Kang |
| Tips≠ | Supervised / unsupervised image classification | Local spatial regression |
| Pirmavots≠ | Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley. ISBN: 978-1118343289 | Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI ↗ |
| Citi nosaukumi | land cover classification, image classification, satellite image classification, spectral classification | MGWR, multiscale GWR, multi-scale geographically weighted regression, variable-bandwidth GWR |
| 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. | Multiscale Geographically Weighted Regression (MGWR) is a local spatial regression framework that relaxes the single-bandwidth constraint of standard GWR by allowing each predictor to operate at its own spatial scale. Each coefficient surface is calibrated with its own bandwidth, enabling the model to distinguish drivers that vary slowly across space from those that vary sharply. |
| ScholarGateDatu kopa ↗ |
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