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	Detekcja zmian	Metryki krajobrazu	Klasyfikacja obrazów oparta na pikselach
Dziedzina	Teledetekcja	Analiza przestrzenna	Teledetekcja
Rodzina	Process / pipeline	Process / pipeline	Machine learning
Rok powstania	1989	1988	2007
Twórca	Ashbindu Singh	R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)	Remote-sensing classification literature
Typ	Multitemporal image comparison pipeline	Quantitative landscape pattern description	Supervised/unsupervised spectral image classification
Źródło pierwotne	Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.	O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.	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.
Inne nazwy	Multitemporal Image Analysis, Land-Cover Change Analysis, Bitemporal Change Analysis, Değişim Tespiti	landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
Pokrewne	2	3	2
Podsumowanie	Change detection is a remote sensing analysis pipeline that identifies differences in land cover or land use between two or more images acquired at different times over the same geographic area. Systematically reviewed and classified by Ashbindu Singh in 1989, the framework encompasses image differencing, post-classification comparison, vegetation index differencing, and principal component analysis, and remains the canonical reference for evaluating which technique best suits a given application.	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.