Object-oriented classification and application in land use classification using SPOT-5 PAN imagery

Abstract

High-resolution remotely sensed data have been actively employed in urban land use/cover. Object-oriented classification techniques based on image segmentation are being actively studied in the high-resolution image process and interpretation to extract a variety of thematic information. Different from the pixel-based image analysis, the processing of the object-oriented method is based on image object or segment, not single pixel. The object-oriented classification includes two consecutive processes. An image is subdivided into separated regions according to the spectral and spatial heterogeneity in the image segmentation process. Then the objects are assigned to a specific class according to the class's detailed description in the image classification process. As a case study, the study area is a pail of the planning Beijing Olympic Games Cottage, which has changed greatly with the advent of the year of 2008. The panchromatic SPOT-5 image in August of 2002 is segmented and these segments then are classified to hierarchically linked objects by the eCognition software. The overall classification accuracy is up to 87%.