Attentional Dense Convolutional Neural Network for Water Body Extraction From Sentinel-2 Images

Monitoring water bodies from remote sensing data is certainly an essential task to supervise the actual conditions of the available water resources for environment conservation, sustainable development, and many other applications. Being Sentinel-2 images some of the most attractive data, existing traditional index-based and deep learning-based water extraction methods still have important limitations in effectively dealing with large heterogeneous areas since many types of water bodies with different spatial-spectral complexities are logically expected. Note that, in this scenario, optimal feature abstraction and neighborhood information may certainly vary from water to water pixel, however existing methods are generally constrained by a fix abstraction level and amount of land cover context. To address these issues, this article presents a new attentional dense convolutional neural network (AD-CNN) especially designed for water body extraction from Sentinel-2 imagery. On the one hand, the AD-CNN exploits dense connections to allow uncovering deeper features while simultaneously characterizing multiple data complexities. On the other hand, the proposed model also implements a new residual attention module to dynamically put the focus on the most relevant spatial-spectral features for classifying water pixels. To test the performance of the AD-CNN, a new water database of Nepal (WaterPAL) is also built. The conducted experiments reveal the competitive performance of the proposed architecture with respect to several traditional index-based and state-of-the-art deep learning-based water extraction models.

Automated summary

This article presents a new attentional dense convolutional neural network (AD-CNN) for water body extraction from Sentinel-2 imagery. The AD-CNN exploits dense connections to uncover deeper features and implements a residual attention module to dynamically focus on relevant features. Experimental results show that the proposed method achieves competitive performance.