Semantic Segmentation for Waterbody Extraction Using Superpixels and Convolutional Neural Networks Classifier

Waterbody extraction from satellite images is an important task for many applications, such as hydrological modeling, ecosystem monitoring and water reserve level tracking.To tackle this problem, several deep learning based approaches have been proposed in the literature.However, these approaches have difficulty delineating water bodies due to their variations in color, size and shape.To overcome this limitation, a novel deep learning-based approach that leverages the power of Convolutional Neural Networks (CNNs) and Superpixel technique using Simple Linear Iterative Clustering (SLIC) algorithm is proposed.The proposed method involves an initial over-segmentation of the input satellite image into homogeneous zones using the SLIC algorithm.These zones are then further processed to extract Regions Of Interest (ROI) that are classified as either water or non-water using a CNN model.Finally, each pixel within a homogeneous zone is assigned the predicted class of its associated ROI.The obtained results using Gaofen Image Dataset show the effectiveness of the proposed approach, while highlighting its superiority over state-of-the-art (SOTA) approaches.