Bruzzone, Lorenzo and Fernandez Prieto, Diego (2002) An Adaptive Semi-Parametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images. UNSPECIFIED.
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.
|Item Type: ||Departmental Technical Report|
|Department or Research center: ||Information Engineering and Computer Science|
|Subjects: ||T Technology > T Technology (General)|
|Uncontrolled Keywords: ||change detection, multitemporal images, remote sensing, adaptive semi-parametric estimation, Bayes theory, reduced Parzen estimate, expectation-maximization algorithm|
|Additional Information: ||Appeared on IEEE Transactions on Image Processing, Vol. 11, No. 4, 2002|
|Report Number: ||DIT-02-030|
|Repository staff approval on: ||21 Jan 2003|
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