Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Классификация изображений дистанционного зондирования× | Локальные индикаторы пространственной ассоциации (LISA)× | |
|---|---|---|
| Область | Пространственный анализ | Пространственный анализ |
| Семейство | Regression model | Regression model |
| Год появления≠ | 1970s–present | 1995 |
| Автор метода≠ | Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments) | Luc Anselin |
| Тип≠ | Supervised / unsupervised image classification | Local spatial statistic |
| Основополагающий источник≠ | Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley. ISBN: 978-1118343289 | Anselin, L. (1995). Local Indicators of Spatial Association — LISA. Geographical Analysis, 27(2), 93–115. DOI ↗ |
| Другие названия | land cover classification, image classification, satellite image classification, spectral classification | LISA, local spatial autocorrelation statistics, local Moran's I, Anselin LISA |
| Связанные≠ | 4 | 6 |
| Сводка≠ | 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. | LISA, introduced by Luc Anselin in 1995, decomposes a global spatial autocorrelation index into a location-specific statistic for every observation. It identifies where statistically significant spatial clusters and outliers occur on a map, enabling researchers to move beyond a single global summary and pinpoint the geographic sources of spatial dependence. |
| ScholarGateНабор данных ↗ |
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