The Role of Pan-Sharpening in Coastline Extraction from High-Resolution Satellite Images

Coastline extraction from satellite imagery plays a vital role in environmental monitoring and coastal management. One of the most common approaches involves the use of indices such as the Normalized Difference Water Index (NDWI) and its variants, which effectively differentiate water bodies from land areas. For certain types of high-resolution satellite images, pan-sharpening techniques can further enhance the results. Pan-sharpening is a data fusion method that integrates the spectral information of multispectral images with the superior spatial resolution of panchromatic images. Despite its advantages, the benefits of pan-sharpening may diminish or even vanish when normalized indices are computed from the pan-sharpened images, potentially affecting the accuracy of coastline extraction. This study investigates the effectiveness of different pan-sharpening techniques for coastline detection derived from a normalized water index. The research focuses on a portion of the metropolitan area of Naples, Italy, using highresolution IKONOS satellite imagery. Three widely used pansharpening methods are evaluated: Intensity-Hue-Saturation (IHS), Brovey Transformation (BT), and Gram-Schmidt Transformation (GST). Results indicate that the BT is entirely ineffective, with its enhancement disappearing completely when the index is applied. The IHS method shows a mild improvement, offering limited spatial enhancement. In contrast, the GST emerges as the most effective technique, preserving spatial and spectral characteristics and yielding the clearest and most accurate coastline delineation.