Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Метрики ландшафтного паттерна× | Объектно-ориентированный анализ изображений (OBIA)× | |
|---|---|---|
| Область≠ | Пространственный анализ | Дистанционное зондирование |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1988 | 2010 |
| Автор метода≠ | R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS) | Thomas Blaschke |
| Тип≠ | Quantitative landscape pattern description | Image segmentation and classification pipeline |
| Основополагающий источник≠ | O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162. DOI ↗ | Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16. DOI ↗ |
| Другие названия | landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri | Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi |
| Связанные | 3 | 3 |
| Сводка≠ | Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis. | Object-Based Image Analysis (OBIA) is a remote sensing image processing paradigm that groups pixels into meaningful image objects before classification, rather than analysing each pixel independently. Formally articulated and consolidated by Thomas Blaschke in his landmark 2010 ISPRS review, OBIA draws on multiresolution segmentation algorithms and combines spectral, spatial, contextual, and textural object attributes to produce semantically rich land-cover maps from high-resolution imagery. |
| ScholarGateНабор данных ↗ |
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