Application of Spectral Mixture Analysis to Urban Change Detection

Meysam Argany
Remote Sensing Engineer
Remote Sensing Division, Surveying and Geomatics Engineering Department, Faculty of Engineering, University of Tehran,
Iran
Email: margani@ut.ac.ir


The light spectrum that is recorded by a pixel on an imaging sensor is often a mixture of several distinct source spectra. Such a mixture may be modeled as a linear combination of some basis spectra, and if the basis for the scene is known, each pixel may be decomposed into its original components using linear inversion. This paper reports on preliminary results from a study applying the technique of spectral mixture analysis (SMA) to the measurement of temporal changes in the composition of urban land use in the area of Greater Karaj, Iran, between 1987 and 2000. Although several remote sensing techniques have been used successfully for urban change analysis, most of these focus on change ‘between’ classes measured in a discrete, crisp way through which each pixel is assigned to a label indicating either a change or no change in the class to which the pixel originally belonged. In many major cities, such as Karaj, change also occurs within classes and is reflected by an aggregation of land use and urban materials. SMA is demonstrated to be capable of deriving spatially continuous variables quantified at the sub-pixel level. These variables represent measures that can be compared across urban places and at different time periods.