Object-based change detection and classification
Irmgard Niemeyer, Florian Bachmann, André John, Clemens Listner, Prashanth Marpu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
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.