Enhanced Multi-Level Deep Fusion UNET with Dual Attention for Land Cover Segmentation

The land use land cover dataset is created using images from ArcGIS, categorized into barren land, crop land, vegetation and water bodies. The dataset is preprocessed for accuracy and diversity, and then used for land cover segmentation and classification using a highly enhanced multi-level deep fused U-Net with dual attention framework (MLDUNet-DA). The proposed architecture includes fused dilated convolution, multi-kernel-based feature recognition and Adaptive Batch Normalization. The training efficiency is improved by an adaptive weight decay factor, and dual attention enhances classification performance to strengthen the generalization of the U-Net segmentation using a Self-adaptive Hippopotamus optimization algorithm. The model is implemented on the Python platform and analyzed for accuracy, Dice score, Jaccard, IoU (Intersection Over Union), precision, recall and F1-Measure.