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Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

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journal contribution
posted on 24.03.2021, 22:20 by RG Negri, Alejandro Frery
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature. The Earth’s environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, remote sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated datasets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50% the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

History

Preferred citation

Negri, R. G. & Frery, A. C. (2021). Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach. Pattern Analysis and Applications, 1-17. https://doi.org/10.1007/s10044-020-00954-w

Journal title

Pattern Analysis and Applications

Publication date

01/01/2021

Pagination

1-17

Publisher

Springer Science and Business Media LLC

Publication status

Published

Online publication date

06/01/2021

ISSN

1433-7541

eISSN

1433-755X

Language

en

Exports