ABSTRACT
Today, information on land cover and land use is almost exclusively derived from remotely sensed observations at various spatial and temporal scales. Ÿe advantages of these observations include, but not limited to, synoptic view, availability of spectral bands that help distinguish land surface properties, archived temporal record, and digital nature. Ÿe principle form of deriving land-cover information from remotely sensed images is classi¦cation. In the context of remote sensing, classi¦cation refers to the process of translating observations into land-cover categories with clearly de¦ned biogeophysical function. For example, a typical land-cover map may contain categories like forest, water, agriculture, and so on. Ÿese maps are then used in a growing number of environmental applications, from resource management to global change studies. To this end, the purpose of this chapter is to review existing and emerging image classi¦cation methods applied to remote sensing.
