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Water monitoring is an important part of water resource protection. The extraction of water body from multispectral remote-sensing images has been proven to be an efficient and fast way for water monitoring. This paper presents a water body extraction algorithm from multispectral remote-sensing image based on region similarity and boundary information by combining adaptive band selection and over-segmentation. First of all, three bands are adaptively chosen by similarity-based band selection algorithm. Then, the image domain is partitioned into a series of homogeneous sub-regions by over-segmentation incorporating spectral and spatial information. On the sub-regions, the regional similarity is defined with respect to the similarities of texture and spectral features which are extracted using structure analysis method. After that, boundary information is extraction by Canny algorithm, then the water body is extracted by using the Fractal Net Evolution Approach (FNEA) which combines regional similarity and boundary information. The proposed algorithm is used to extract six water bodies with different complex texture backgrounds from multispectral sensors. According to the accuracy evaluation of water body extraction results, the overall accuracy (OA) is higher than 97.9100% and all Kappa coefficients (K) are up to 0.9436. We calculated the relative error (RE) of the area between the reference water body and the water body extracted by the proposed algorithm, the minimum and maximum relative error range is between [0.6180%, 7.7050%]. The experiments show that the proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]

Monitoring the spatial and temporal extents of permanent and temporary bodies of surface water is important for various applications such as water resource management, climate modelling, and biodiversity conservation. Satellite remote sensing is an effective source of information to detect surface water over large areas and document their evolution in time. Recently, the European Space Agency (ESA) launched freely available SAR (Synthetic Aperture Radar) and optical sensors (Sentinel-1 & 2) with high revisiting time and spatial resolution. The objective of this paper is to explore the contribution of multi-temporal and multi-source (passive and active) Sentinel observations for improving the detection and mapping of surface waters by applying decision-level image fusion techniques. The approach is tested over Central Ireland using a time series of 16 Sentinel-1 images and a few Sentinel-2 images for the period 2015–2016. Compared to a mono-date approach, the combination of Sentinel-1 & 2 observations provides better accuracy for mapping permanent surface water. Decision level fusion technique allows mapping temporary surface water (such as flooding) with a high accuracy. It also gives the possibility to monitor their dynamics by providing the probability of occurrence of flooded areas at the pixel level. [ABSTRACT FROM AUTHOR]