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| 景观格局指数× | 基于像素的图像分类× | |
|---|---|---|
| 领域≠ | 空间分析 | 遥感 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 1988 | 2007 |
| 提出者≠ | R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS) | Remote-sensing classification literature |
| 类型≠ | Quantitative landscape pattern description | Supervised/unsupervised spectral image classification |
| 开创性文献≠ | O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162. DOI ↗ | Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. DOI ↗ |
| 别名 | landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri | Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma |
| 相关≠ | 3 | 2 |
| 摘要≠ | 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. | Pixel-based image classification is a fundamental remote-sensing technique that assigns each individual pixel in a satellite or aerial image to a thematic land-cover category based solely on its spectral values across multiple bands. Systematically surveyed and formalized by Lu and Weng (2007), the approach encompasses both supervised methods—where labeled training samples guide the classifier—and unsupervised clustering approaches that discover natural spectral groupings without prior labels. |
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