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Erschienen in:
2008 | OriginalPaper | Buchkapitel
Change detection using object features
verfasst von : I. Niemeyer, P.R. Marpu, S. Nussbaum
Erschienen in: Object-Based Image Analysis
Verlag: Springer Berlin Heidelberg
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For the detection of changes, several statistical techniques exist. When adopted to high-resolution imagery, the results of traditional pixel-based algorithms are often limited. We propose an unsupervised change detection and classification procedure based on object features. Following the automatic pre-processing of the image data, image objects and their object features are extracted. Change detection is performed by the multivariate alteration detection (MAD), accompanied by the maximum autocorrelation factor (MAF) transformation. The change objects are then classified using the fuzzy maximum likelihood estimation (FMLE). Finally the classification of changes is improved by probabilistic label relaxation.