Water, as one of the most essential resources on the Earth, not only
sustains human life and ecological cycles, but also plays a vital role
in economic development and mineral exploration (Vorosmarty et al.,
2000).Therefore, constructing large-scale to over all
water conditions, combining multi-modal and multi-temporal RS data
and domain knowledge to improve the interpretation generalization
in other areas and long-term perception ability would be the future
research directions of water body classification.In this context, water
identification aims at classifying if a pixel from RS images is water or
not, which has gradually evolved into a hot research topic in RS, and
scholars have carried out many studies to extract water bodies from
various RS images (Hollstein et al., 2016; Dang and Li, 2021).Focusing on multiple-sensor images processing, Li et al. (2021c) presented an encoder-decoder-based dense-localfeature-compression (DLFC) network to extract valuable spatial and
spectral details.How to enhance
the interpretation effect with limited samples has always been a hot
topic for scholars in RS. Li et al. (2021b) used a region of interest
(ROI) to build water labels and then proposed a pixel-based CNN
to synchronously combine spectral and texture information for water
segmentation.To fully use microwave images, Xue et al. (2021) introduced a densecoordinate-feature concatenate network (DCFCN) for merging water
body features from dual-polarimetric SAR images and thus address the
ground interference in single-polarization SAR images. A portion of this is surface water and mainly involves rivers,
lakes, canals, and ponds; the oceans are always excluded from this
category due to their large extent and salty characteristic (Huang et al.,
2018).Later, many improved water indexes
were developed, such as the modified NDWI (MNDWI) (Xu, 2006),
EWI (Yan et al., 2007), NWI (Feng, 2012), HRWI (Yao et al., 2015)
and two-step TSUWI (Wu et al., 2018).Next, a restricted receptive field
DeconvNet (RRF DeconvNet) was presented to compresses redundant
layers and then uses an edge weighting loss to extract accurate water
edges (Miao et al., 2018).With
the growth of a severe global water shortage and increasing flood disasters, the efficient and accurate assessment of available surface water has
become an essential part of ecological protection, urban planning, and
industrial production.Over the past decades, Earth observation techniques have advanced significantly and many kinds of high-resolution
RS images are accessible for various real-world applications, including
efficiently identifying available water bodies.Therefore, data-driven ML- and DL based methods can adaptively leverage spectral and spatial information to build
discriminative features for efficient and accurate water classification.Due
to the simplicity of DT, Hollstein et al. (2016) developed several DTbased classification methods for simultaneously extracting water, snow,
clouds, and so on from Sentinel 2 images.Differently, (Morsy et al., 2018) designed a unsupervised
land/water classification method to automatically monitor the changes
of coastal areas from multi-spectral airborne LiDAR data.Isikdogan et al. (2017) demonstrated an early application
of a data-driven FCN for surface water mapping of Landsat 7 images,
which was different from the threshold methods in adopting different regions and imaging conditions.