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：2004
卷号：XXXV Part B3
页码：325-330
出版社：Copernicus Publications
摘要：This paper deals with a methodology to derive object models for automatic object extraction in low resolution images from models created manually for high resolution images. The object models are represented by semantic nets, which describe landscape objects explicitly in terms of natural language. Starting from semantic nets for high resolution images the strategy is to first decompose them into parts, which can be handled autonomously. The object parts are then adapted, i.e. generalised, to smaller scale. The adaptation takes into account the object shape, radiometry, and texture. For the generalisation process "scale change models" are used, which describe how different types of objects evolve over scale mathematically. Finally, all object parts are fused and transferred to a semantic net representation. In this paper first results of the described methodology are presented. Focussing on line-type objects, such as streets, we describe how to create an object description with semantic nets using constraints, which have to be satisfied, in order to be able to adapt the nets to other scales automatically. In addition we show tests of the behaviour of some edge- and line- extraction operators through scale space. These tests are necessary to predict the scale behaviour of different object types. At last, we describe as an example for a particular object events during scale change observed in an image and their impact on a semantic net. This example demonstrates the suitability of the proposed kind of semantic net to follow the scale space events in digital images, and thus, its applicability in an automatic approach
