Dual-Branch ConvNeXt-Based Network with Attentional Fusion Decoding for Land Cover Classification Using Multispectral Imagery

This work presents an encoder-decoder architecture designed for Land Cover Classification utilizing Multispectral Imagery. The architecture consists of two parallel feature extraction branches based on ConvNeXt, one dedicated to processing RGB information and the other to NIR data. The encoders are connected with a spectral fusion and pyramidal decoding process enhanced with a convolutional block attention module, which emphasizes the most relevant features to produce accurate segmentation masks. Experiments are conducted using the Potsdam dataset, and results demonstrate that the proposed approach achieves notable performance, with an overall accuracy of 90.83% and a mean intersection over union of 77.69%. Furthermore, visual tests show that the proposed approach is better at differentiating complex classes, achieving segmentations closer to the ground-truth. The method outperforms other well-known architectures, including U-Net, PSPNet, DeepLabV3, and DeepLabV3+, as well as other state-of-the-art approaches. Code is available at https://github.com/Leo-Thomas/mecafnet.git