Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Глобальная классификация данных дистанционного зондирования× | Пространственная автокорреляция× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970s–1980s (pixel-based global classifiers); global land-cover products 1990s–2000s | 1950 |
| Автор метода≠ | Rosenfeld & Kak; Jensen; Campbell & Wynne (textbook codifications) | P. A. P. Moran (global measure, 1950); Roy Geary (Geary's C, 1954); Luc Anselin (LISA, 1995) |
| Тип≠ | Supervised / unsupervised image classification | Spatial statistic / exploratory spatial data analysis |
| Основополагающий источник≠ | Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing (5th ed.). Guilford Press. ISBN: 978-1609181765 | Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17–23. DOI ↗ |
| Другие названия | global pixel-based classification, global image classification, wall-to-wall remote sensing classification, global land cover classification | spatial dependence, geographic autocorrelation, spatial clustering measure, SA |
| Связанные≠ | 3 | 5 |
| Сводка≠ | Global Remote Sensing Classification assigns every pixel across an entire image or worldwide dataset to a discrete land-cover or thematic class. Treating the scene uniformly — rather than adapting to local subregions — this wall-to-wall approach underpins continental and global land-cover products such as GlobCover, FROM-GLC, and ESA CCI Land Cover. | Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal clusters and outliers at the level of individual observations. |
| ScholarGateНабор данных ↗ |
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