Advanced Multi-Scale Enhanced U-Net for Efficient Land Cover Classification of Remote Sensing Images

For monitoring the environment, building cities, assessing crops, and studying the climate, it is very important to be able to accurately classify land cover from remote sensing images. Deep learning has made semantic segmentation work much better, especially with encoder-decoder designs like U-Net. Still, ordinary U-Net models have trouble capturing multi-scale contextual relationships, distinguishing narrow borders, and successfully emphasizing traits that are distinctive to an area. This work presents an Advanced Multi-Scale Enhanced U-Net (AMSE-U-Net) to address these difficulties. The AMSE-U-Net combines (i) multi-scale feature extraction, (ii) squeeze-and-excitation channel attention, and (iii) attention-gated skip connections. The model improves learning of both local and global features while getting rid of background noise that isn’t useful. Tests done on common remote sensing datasets show big improvements in Intersection over Union (IoU), pixel precision, and boundary delineation when compared to standard U-Net and similar models. The suggested AMSE-U-Net works better for generalization with only a little amount of extra processing power, making it good for monitoring land cover and the environment.