Journal
PATTERN RECOGNITION LETTERS
Volume 28, Issue 4, Pages 405-413Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.patrec.2006.08.010
Keywords
neural networks; change detection; remote sensing
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In this paper a new approach to performing change detection analyses based on a combination of supervised and unsupervised techniques is presented. Two remotely sensed, independently classified images are compared. The change estimation is performed according to the Post Classification Comparison (PCC) method if the posterior probability values are sufficiently high; otherwise a land cover transition matrix, automatically obtained from data, is used. The proposed technique is compared with the traditional PCC approach. It is shown that the new approach correctly detects the true change without overestimating the false one, while PCC points out true change pixels together with a large number of false changes. (c) 2006 Elsevier B.V. All rights reserved.
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