Detecting Distribution of Industrial Areas using Spectral Mixture Analysis (SMA) of Landsat-ETM Satellite Image

Abstract

The current process of geographical data collection, such as industrial and settlement are as, is mostly performed by implementing terrestrial mapping method. The new solution for this process is offered by the remote sensing method. It is based upon the ability of this technique to observe the wide-ranged earth surface objects from a distance in a relatively short period and using a broad spectrum of spatial, spectral and temporal resolutions. The quality improvement of remote sensing final product has become a need and it has to be adequate to the user satisfaction. There is a new technique that has been developed to increase the ability of remote sensing application on object identification, which is called SMA. In this study, the data acquisition process of industrial and settlement areas is made by applying SMA on Landsat ETM image. The object is identified based on sub pixel component that produces industrial and settlement areas end member images, which includes as well the proportion of each end member.. The results indicate that linear mixture modeling can be used to identify industrial areas within mixed pixels.