In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semi-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semi-parametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach.

An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images / Bruzzone, Lorenzo; Fernandez Prieto, Diego. - ELETTRONICO. - (2002), pp. 1-37.

An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images

Bruzzone, Lorenzo;
2002-01-01

Abstract

In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semi-parametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semi-parametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach.
2002
Trento, Italia
Università degli Studi di Trento. DEPARTMENT OF INFORMATION AND COMMUNICATION TECHNOLOGY
An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images / Bruzzone, Lorenzo; Fernandez Prieto, Diego. - ELETTRONICO. - (2002), pp. 1-37.
Bruzzone, Lorenzo; Fernandez Prieto, Diego
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/358456
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