Evaluation of spectral similarity indices in unsupervised change detection approaches
Unsupervised change detection (UCD) is a subject of Remote Sensing whose objective is to detect the differences between two multi-temporal images. In some cases, spectral similarity indices have been used as the comparison block in algorithms of UCD. The aim of this paper is to show in a quantitativ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad Nacional de Colombia
2018-01-01
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| Series: | Dyna |
| Subjects: | |
| Online Access: | https://revistas.unal.edu.co/index.php/dyna/article/view/68355 |
| Summary: | Unsupervised change detection (UCD) is a subject of Remote Sensing whose objective is to detect the differences between two multi-temporal images. In some cases, spectral similarity indices have been used as the comparison block in algorithms of UCD. The aim of this paper is to show in a quantitative way the performance of four spectral similarity indices in the correct identification of changes. Comparison is performed in terms of precision (overall accuracy and kappa index) over medium and high-resolution images (SPOT-5: Satellite Pour l'Observation de la Terre and Quickbird), with a reference obtained through a post-classification method (based on Support Vector Machines, SVM). The results show dependence on the automatic thresholding technique, as well as on the classes associated with the change. |
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| ISSN: | 0012-7353 2346-2183 |