Unsupervised classification is one of the two basic approaches to digital image classification with the goal of producing land cover maps from remotely sensed data. The other is supervised classification. Unsupervised algorithms evaluate the spectral properties of image pixels and segregate them into naturally occurring statistical groups with little or no guidance from the analyst. The underlying assumption is that a remotely sensed landscape comprises a mosaic of land cover classes, each of which is relatively uniform in terms of spectral pattern. Following image classification, a significant challenge for the analyst is to apply meaningful land cover labels to the spectral classes identified. This entry summarizes the unsupervised classification logic, briefly examines two commonly used algorithms, and discusses the primary advantage and disadvantage of the ...