Research Article
Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts: A Fuzzy Multiobjective Approach
Table 1
Quantitative results for semisynthetic data sets with haze and thin cloud.
| Method | Figures 5(a) and 3(b) | Figures 5(b) and 3(b) | Figures 5(c) and 3(b) | Figures 5(d) and 3(b) | | | | | | | | | | | | |
| DT-CWT-based | 32.27 | 10.88 | 15.02 | 31.05 | 9.65 | 15.02 | 35.03 | 13.23 | 15.75 | 40.23 | 12.65 | 22.91 | PCA- means | 28.66 | 7.93 | 12.45 | 25.37 | 6.55 | 10.40 | 29.36 | 10.49 | 13.02 | 34.23 | 12.43 | 20.19 | ERGAS-based | 4.43 | 21.31 | 8.53 | 5.67 | 15.14 | 6.64 | 3.05 | 10.68 | 5.81 | 20.22 | 7.84 | 13.43 | PSO-GA-based | 16.09 | 4.19 | 8.18 | 12.57 | 4.24 | 5.90 | 7.87 | 2.95 | 4.96 | 14.55 | 16.41 | 15.25 | Proposed Method | 3.98 | 2.73 | 3.38 | 2.71 | 2.09 | 2.44 | 1.91 | 2.18 | 2.02 | 2.83 | 4.03 | 3.56 |
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