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Proceedings Paper

Object-based change detection and classification
Author(s): Irmgard Niemeyer; Florian Bachmann; André John; Clemens Listner; Prashanth Reddy Marpu
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Paper 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.

Paper Details

Date Published: 28 September 2009
PDF: 11 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770S (28 September 2009); doi: 10.1117/12.830409
Show Author Affiliations
Irmgard Niemeyer, Technische Univ. Bergakademie Freiberg (Germany)
Florian Bachmann, Technische Univ. Bergakademie Freiberg (Germany)
André John, Technische Univ. Bergakademie Freiberg (Germany)
Clemens Listner, Technische Univ. Bergakademie Freiberg (Germany)
Prashanth Reddy Marpu, Univ. of Pavia (Italy)

Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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