A Reduced Rank Regression Mixture Model for Change Validation in Aerial Images
نویسنده
چکیده
Change detection is an important part of image interpretation and automated geographical data collection. In this paper we show a reduced rank regression mixture model for the verification of image changes detected by a human operator. Maximum likelihood estimators are used to learn the operator behaviour. Then, the operator uses the trained system to validate the image changes found. Computational results are given with real image data that show the performance of the system.
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