All-Weather and Superpixel Water Extraction Methods Based on Multisource Remote Sensing Data Fusion

The high spatial and temporal resolution of water body data offers valuable guidance for disaster monitoring and assessment. These data can be employed to quickly identify water bodies, especially small water bodies, and to accurately locate affected areas, which is significant for protecting people's lives and property. However, the application of optical remote sensing is often limited by clouds and fog during actual floods. In this paper, water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM) are proposed, in which the MDFM fuses optical and synthetic aperture radar (SAR) images, and all-weather water extraction is achieved by using spectral information of optical images, texture information and the good penetration performance of SAR images. The SWEM further improves the accuracy of the water boundary with superpixel decomposition for extracted water boundaries using the fully constrained least squares (FCLS) method. The results show that the correlation coefficient (r) and area accuracy (P-area) of the MDFM and SWEM are improved by 2.22% and 9.20% (without clouds), respectively, and 3.61% and 18.99% (with clouds), respectively, compared with the MDFM, and 41.54% and 85.09% (without clouds), respectively, and 32.31% and 84.31% (with clouds), respectively, compared with the global surface water product of the European Commission Joint Research Centre's Global Surface Water Explorer (JRC-GSWE). The MDFM and SWEM can extract water bodies with all weather and superpixel and improve the temporal and spatial resolution of water extraction, which has obvious advantages.

Automated summary

The high spatial and temporal resolution of water body data is valuable for disaster monitoring and assessment. This paper proposes the water extraction methods of the multisource data fusion model (MDFM) and superpixel water extraction model (SWEM), which can overcome the limitations of optical remote sensing during floods and improve the accuracy and resolution of water extraction.