期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B4
页码：187-192
出版社：Copernicus Publications
摘要：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-vs- rest 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
关键词：Landscape; Segmentation; Features; Extraction; Classification; Modelling; Data Mining
