Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data

Abstract

The incorporation of a red edge channel in multi-spectral satellite sensors has potential for improving land-use classification, as the related electromagnetic spectrum is specifically sensitive to vegetation chlorophyll content. RapidEye is the first high-resolution multi-spectral satellite system that operationally provides a red edge channel. The objective of this study is to test the potential of the RapidEye red edge channel for improving the classification of land use, investigated at a study site west of Berlin. Based on a scene from July 2009, supervised land-use classifications were performed using different sets of spectral feature input, including and excluding red edge information. The algorithms used are support vector machine and maximum likelihood. The results indicate that the incorporation of red edge information can increase classification accuracy. The highest positive effects are observed for vegetation classes in open landscapes, e.g. for bush vegetation.