研究目的
To propose an automatic and unsupervised method for water body extraction from GF-1 multispectral imagery by integrating spectral and spatial features to address issues like confusion with built-up lands and border pixel removal.
研究成果
The proposed MFWE method effectively integrates spectral and spatial features for accurate water body extraction, outperforming existing methods like NDWI thresholding and SVM classifications in terms of accuracy and reducing commission and omission errors. Future work will focus on adaptive parameter tuning and sensitivity analysis.
研究不足
The method relies on fixed thresholds (T1, T2, T3) which may not be adaptive to all scenarios; it is specific to GF-1 imagery and may require parameter tuning for other sensors; unsupervised nature might not handle extremely complex backgrounds as well as supervised methods with large training sets.
1：Experimental Design and Method Selection:
The method integrates spectral (NDWI) and spatial (PRI) features in three stages: obtaining a major water body mask using PRI and NDWI, clustering pixels with k-means to create a water guide map, and merging them for the final water mask. It is unsupervised and automatic.
2：Sample Selection and Data Sources:
Three GF-1 multispectral images were used, covering areas like Bohai Sea, Qiantang River, and Yangtze River, with manually created reference maps from Google Earth.
3：List of Experimental Equipment and Materials:
GF-1 satellite imagery (multispectral bands: blue, green, red, NIR), computational tools for image processing (e.g., for k-means clustering, NDWI calculation).
4：Experimental Procedures and Operational Workflow:
Input radiometric corrected GF-1 image; calculate PRI for each pixel using Algorithm 1 with thresholds T1=40, T2=100, T3=5; classify pixels based on PRI; compute NDWI and apply peaks-valley method for water detection; use k-means clustering (cluster number=10) on remaining pixels with guidance from major water mask; merge results to final water mask.
5：Data Analysis Methods:
Quantitative comparison with NDWI-OTSU, SVM-S, and SVM-C methods using confusion matrix metrics (PA, UA, OA, Kappa) based on pixel-wise accuracy against reference maps.
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