Water Body Extraction Method Based on ConvNeXt and Dual Feature Extraction Branch

Due to the combined effects of complex spectral mixtures, blurred boundaries of ground objects, and environmental noise, it is extremely challenging to accurately identify water boundaries from high-resolution remote sensing images. To address this problem, this paper proposes a water body extraction method based on ConvNeXt and dual feature extraction branch (CoNFM-Net) on the basis of PSPNet. In the encoder stage, ConvNeXt is used instead of ResNet50 as the backbone network, which uses inverted bottleneck layer, large kernel and other designs to enhance the feature extraction ability of the network. In the decoder stage, a dual feature extraction branch structure with multi-scale feature fusion and context information enhancement is designed. In order to effectively utilize the multi-level feature map generated by the backbone network, a bidirectional feature fusion module (BiFFM) is designed to solve the problem of scale inconsistency in boundary recognition. Aiming to improve the utilization rate of global information, the deep feature map output by the backbone network is passed through the global context information module (GCIM). At the same time, the deepest feature map of the multi-scale feature fusion branch is spliced with it to enhance the model’s ability to capture the details of the water boundary. Experimental results show that the mean intersection over union and F1-score of this method on LoveDA dataset, GF-2 dataset and Sentinel-2 dataset are 89.64%, 94.32%, 92.60%, 96.16% and 93.72%, 96.73%, respectively. In the same environment, compared with U-Net, DANet, CMTFNet and other semantic segmentation algorithms, the proposed algorithm CoNFM-Net has certain advantages.