What is an unsupervised classification?
¿Qué es la clasificación no supervisada?
From an engineering perspective, the simplest use of optical remotely sensed imagery is on the interpretation and identification of historical changes of land surface features (e.g., on the area covered by a certain kind of forest). Using simple words, such task can be accomplished by classifying the pixels of the imagery into as many groups as needed, or more properly, as allowed by the resolution of the imagery. There are two kinds of classification procedures: the unsupervised classification and the supervised classification. The supervised classification is principally used to extract qualitative information out from the imagery, having enough knowledge of the physical characteristics of some pixels, which can be used to characterize or classify other pixels within the study area. On the other hand, the unsupervised classification, simply generates clusters of pixels, that qualitatively characterize the study area. The latter is commonly used when there is not enough knowledge on the physical characteristics of the pixels (e.g., when performing multi temporal analysis of surface areas along a wide historical lapse of time, for which there is limited knowledge and/or information). An interesting example can be found following the link of the figure above. In this entry, I am presenting a tutorial of how to perform the latter (unsupervised classification). The material has been originally prepared by the consultants C. Buitrón and J. Fernández, under my supervision. This tutorial follows the series published on the entries presented before on this blog. Enjoy!
