文章基本信息

标题：COMPARISON OF OBJECT ORIENTED CLASSIFICATION TECHNIQUES AND STANDARD IMAGE ANALYSIS FOR THE USE OF CHANGE DETECTION BETWEEN SPOT MULTISPECTRAL SATELLITE IMAGES AND AERIAL PHOTOS
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作者：Roeland de Kok
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：2000
卷号：XXXIII Part B3(/1+2)
页码：214-221
出版社：Copernicus Publications
摘要：The latest developments in remote sensing analysis software allow new ways of integrating satellite imagery with archive data from aerial photography. This paper shows how this integration is useful in a decision support function in forestry applications in the argentine Nothofagus forests. Two important features are presented. One is the classification of SPOT multispectral data for determination of land cover in Tierra del Fuego. The other is the use of object-oriented classification for change detection using aerial photos combined with SPOT multispectral data. The until now critical objectivity of comparison was achieved by the methodological new approach of object-based classification. A basic description of the new concept of object-oriented classification is given, so far as it is necessary to understand its application in this study. The results for the SPOT data classification are that there are no great differences in accuracy of the two methods. Concerning the appearance of the output maps, the object-oriented method is preferred. The result for the change detection shows that the object-oriented approach offers new possibilities that exceeds the traditional visual interpretation of aerial photography and allows quantitative analysis of change detection and GIS-implementation using an automatic feature extraction
关键词：forestry; change detection; object-oriented; GIS-update; data fusion; SPOT; aerial photos; eCognition