International Society for Photogrammetry and Remote Sensing
Abstract
Satellite image classification has been a major research field for many years with its varied applications in the field of Geography,
Geology, Archaeology, Environmental Sciences and Military purposes. Many different techniques have been proposed to classify
satellite images with color, shape and texture features. Complex indices like Vegetation index (NDVI), Brightness index (BI) or
Urban index (ISU) are used for multi-spectral or hyper-spectral satellite images. In this paper we will show the efficiency of
structural features describing man-made objects in mid-resolution satellite images to describe image content. We will then show the
state-of-the-art to classify large satellite images with structural features computed from road networks and urban regions extracted
on small image patches cut in the large image. Fisher Linear Discriminant (FLD) analysis is used for feature selection and a one-vsrest
probabilistic Gaussian kernel Support Vector Machines (SVM) classification method is used to classify the images. The
classification probabilities associated with each subimage of the large image provide an estimate of the geographical class coverage