השוואת שיטות

	סיווג חישה מרחוק גלובלי	סיווג חישה מרחוק מקצה תוויות דיסקרטיות תמטיות
תחום	ניתוח מרחבי	ניתוח מרחבי
משפחה	Regression model	Regression model
שנת המקור	1970s–1980s (pixel-based global classifiers); global land-cover products 1990s–2000s	1970s–present
הוגה השיטה	Rosenfeld & Kak; Jensen; Campbell & Wynne (textbook codifications)	Swain & Davis (1978); Lillesand & Kiefer (classical textbook treatments)
סוג	Supervised / unsupervised image classification	Supervised / unsupervised image classification
מקור מכונן	Campbell, J. B., & Wynne, R. H. (2011). Introduction to Remote Sensing (5th ed.). Guilford Press. ISBN: 978-1609181765	Lillesand, T. M., Kiefer, R. W., & Chipman, J. W. (2015). Remote Sensing and Image Interpretation (7th ed.). Wiley. ISBN: 978-1118343289
כינויים	global pixel-based classification, global image classification, wall-to-wall remote sensing classification, global land cover classification	land cover classification, image classification, satellite image classification, spectral classification
קשורות	3	4
תקציר	Global Remote Sensing Classification assigns every pixel across an entire image or worldwide dataset to a discrete land-cover or thematic class. Treating the scene uniformly — rather than adapting to local subregions — this wall-to-wall approach underpins continental and global land-cover products such as GlobCover, FROM-GLC, and ESA CCI Land Cover.	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.