Artículos de revistas
Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach
Fecha
2021-01-01Registro en:
Pattern Analysis and Applications.
1433-755X
1433-7541
10.1007/s10044-020-00954-w
2-s2.0-85099041428
Autor
Universidade Estadual Paulista (Unesp)
Victoria University of Wellington
Institución
Resumen
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.