Сравнение методов

	Пиксельная классификация изображений	Объектно-ориентированный анализ изображений (OBIA)
Область	Дистанционное зондирование	Дистанционное зондирование
Семейство	Machine learning	Process / pipeline
Год появления	2007	2010
Автор метода	Remote-sensing classification literature	Thomas Blaschke
Тип	Supervised/unsupervised spectral image classification	Image segmentation and classification pipeline
Основополагающий источник	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.	Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.
Другие названия	Per-Pixel Classification, Spectral Classification, Pixel-by-Pixel Classification, Piksel Tabanlı Sınıflandırma	Geographic Object-Based Image Analysis, GEOBIA, Object-Oriented Image Analysis, Nesne Tabanlı Görüntü Analizi
Связанные	2	3
Сводка	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.	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.