A water body is a common object in remote sensing images and high-quality water body extraction is important for some further applications. With the development of deep learning (DL) in recent years, semantic segmentation technology based on deep convolution neural network (DCNN) brings a new way for automatic and high-quality body extraction from remote sensing images. Although several methods have been proposed, there exist two major problems in water body extraction, especially for high resolution remote sensing images. One is that it is difficult to effectively detect both large and small water bodies simultaneously and accurately predict the edge position of water bodies with DCNN-based methods, and the other is that DL methods need a large number of labeled samples which are often insufficient in practical application. In this paper, a novel SFnet-DA network based on the domain adaptation (DA) embedding selective self-attention (SSA) mechanism and multi-scale feature fusion (MFF) module is proposed to deal with these problems. Specially, the SSA mechanism is used to increase or decrease the space detail and semantic information, respectively, in the bottom-up branches of the network by selective feature enhancement, thus it can improve the detection capability of water bodies with drastic scale change and can prevent the prediction from being affected by other factors, such as roads and green algae. Furthermore, the MFF module is used to accurately acquire edge information by changing the number of the channel of advanced feature branches with a unique fusion method. To skip the labeling work, SFnet-DA reduces the difference in feature distribution between labeled and unlabeled datasets by building an adversarial relationship between the feature extractor and the domain classifier, so that the trained parameters of the labeled datasets can be directly used to predict the unlabeled images. Experimental results demonstrate that the proposed SFnet-DA has better performance on water body segmentation than state-of-the-art methods.

Shadow detection is a basic task of remote sensing image analysis, but it is often seriously disturbed by vegetation, water bodies, and black objects. It is observed that vegetation and dark objects often show a dark look in visible bands but brighter in the near-infrared (NIR), and is also noticed that the reflection of inland water bodies in the green band is stronger than that in the blue band. Taking advantage of these physical properties and combining them with the bluish and dark appearance of shadows, we propose a simple but effective shadow detection method for multispectral remote sensing images. These physical properties are used to create transformation models that suppress features such as vegetation, water bodies, etc., but at the same time enhance shadows. Then, we transform the shadow representation into a color space to generate candidate shadows using dominant color components. To separate shadows from the others, we propose two indexes, the normalized Color Difference Composite Index (CDCI) and Color Purity Index (CPI), and fuse them to achieve shadows and their confidence. The experimental results indicate that the proposed method can effectively detect the shadows in multispectral images and outperforms the state-of-the-art approaches.