Linganisha mbinu

	Utambuzi wa Mabadiliko	Mfumo wa Mabadiliko ya Matumizi ya Ardhi CA-Markov	Uchambuzi wa Picha Kulingana na Vipengele (OBIA)
Nyanja	Utambuzi wa Mbali	Uchanganuzi wa Kimaeneo	Utambuzi wa Mbali
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1989	1997	2010
Mwanzilishi	Ashbindu Singh	Cellular automata (Clarke) + Markov chain (Muller & Middleton)	Thomas Blaschke
Aina	Multitemporal image comparison pipeline	Spatio-temporal land-use change simulation	Image segmentation and classification pipeline
Chanzo asilia	Singh, A. (1989). Digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10(6), 989–1003.	Clarke, K. C., Hoppen, S., & Gaydos, L. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B, 24(2), 247–261.	Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
Majina mbadala	Multitemporal Image Analysis, Land-Cover Change Analysis, Bitemporal Change Analysis, Değişim Tespiti	CA-Markov model, cellular automata Markov, land-use change simulation, CA-Markov arazi kullanımı modeli	Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi
Zinazohusiana	2	3	3
Muhtasari	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.	CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity and the location of change, something neither component does well alone.	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.