سال انتشار: ۱۳۸۱
محل انتشار: دومین کنفرانس ماشین بینایی و پردازش تصویر
تعداد صفحات: ۱۰
Sayyed Bagher Fatemi – KN Toosi Univ. of Technology
Ali A. Abkar – Assistant Professor Soil Conservation and Watershed Management Research Centre Tehran, Iran
In the context of the analysis of remotely sensed data the question arises of how to analyse large volumes of data. In the specific case of agricultural fields in flat areas these fields can often be modelled in terms of geometric primitives such as triangles and rectangles. In this case the options are classical i.e. bottom-up, starting at the pixel level and resulting in a segmented, labelled image or topdown, starting with a model for image partitioning and resulting in a minimum cost estimation of shape hypotheses with corresponding parameters. Standard bottom-up classification methods usually concern the pixel as a main element and try to label the pixel individually. But various errors are involved in the image analysis with these methods. Mixed pixels, simplicity of the basic assumptions in the classification algorithms, sensor effects, atmospheric effects, and radiometric overlap of land cover objects lead to the wrong detection in image analysis. In this paper we propose a Model-Based Image Analysis (MBIA) approach to analyze the remotely sensed data. In this manner using the available knowledge about the remote sensing system we generate some hypothesis maps and then test them using the radiometric measurements (images). In order to test the method we used the boundaries of the agricultural fields stored in a GIS to model the objects in the scene. The results of the method have been compared with the result of a traditional Maximum-Likelihood (MLH) classification and a standard Object-Based Classification (OBC) using the boundaries. Using this approach we could reach to the 94% overall accuracy.