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28 September 2009 Object-based change detection and classification
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Abstract
The paper presents some recent developments on object-based change detection and classification. In detail, the following algorithms were implemented either as Matlab or IDL programmes or as plug-ins for Definiens Developer: i) object-based change detection: segmentation of bitemporal datasets, change detection using the Multivariate Alteration Detection1 based on object features; ii) object features and object feature extraction: moment invariants, automated extraction of object features using Bayesian statistics; iii) object-based classification by neural networks: FFN and Class- dependent FFN using five different learning algorithms. The paper introduces the methodologies, describes the implementation and gives some examples results on the application.
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Irmgard Niemeyer, Florian Bachmann, André John, Clemens Listner, and Prashanth Reddy Marpu "Object-based change detection and classification", Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770S (28 September 2009); https://doi.org/10.1117/12.830409
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