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- W3006007455 abstract "Automatic water feature delineation from satellite imagery is a challenging task due to the variability of sensor parameters, acquisition conditions, and geographical heterogeneity of scenes. To mitigate above challenges, we proposed a fully automatic method for surface water feature dilatation from the moderate resolution satellite imagery through the integration of adaptive and automatic training sample collection by a modulated spectral reflectance curve and one-class classification algorithm. The specific modulation codes that are partially able to represent water features have been chosen to collect positive training samples for the target class. In addition, one-class classification algorithm has applied to classify multispectral imagery, and it utilized the collected datasets as the positive-labeled training samples for the target class. The novelty of the proposed approach is that it's applicable to several moderate resolution sensors having SWIR, NIR, Red, and Green spectral bands. We evaluated the robustness of the proposed method on different multispectral earth observation sensors (Landsat 8OLI, LISS-III IRS-P6, and Sentinel 2AMSI) having heterogeneous acquisition environments. We achieved the overall accuracy on test sites ranges from 93.90 % to 97.27 %, and kappa statistics is ranges from 0.7622 to 0.9453." @default.
- W3006007455 created "2020-02-24" @default.
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- W3006007455 date "2019-03-01" @default.
- W3006007455 modified "2023-09-26" @default.
- W3006007455 title "A Fully Automatic Water Feature Extraction Method with Adaptive Training Samples and One-Class Classification Algorithms" @default.
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