השוואת שיטות

	ניתוח תמונה מבוסס-אובייקטים (OBIA)	מדדי תבנית נוף	סיווג תמונות מבוסס-פיקסלים
תחום	חישה מרחוק	ניתוח מרחבי	חישה מרחוק
משפחה	Process / pipeline	Process / pipeline	Machine learning
שנת המקור	2010	1988	2007
הוגה השיטה	Thomas Blaschke	R. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)	Remote-sensing classification literature
סוג	Image segmentation and classification pipeline	Quantitative landscape pattern description	Supervised/unsupervised spectral image classification
מקור מכונן	Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.	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.
כינויים	Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi	landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma
קשורות	3	3	2
תקציר	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.	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.