Improved approach for Semantic Segmentation of MBRSC aerial Imagery based on Transfer Learning and modified UNet

Aerial imagery has emerged in numerous fields such as sustainable development, forestry, urban planning, agriculture, earth science and climate research. Extracting relevant information from satellite images, such as building detection, road extraction, and land cover classification, is crucial for decision-making. Semantic segmentation (SS),generates a dense pixel-wise segmentation map of a given satellite image where each pixel is related necessarily to a specific class. (SS) has become essential to reach the aforementioned goals. In this context, we presented an improved approach for semantic segmentation of aerial images using the pre-trained CNN VGG16 model and the modified U-Net architecture. Our results show that our proposed approach exceeds state of the art methods in accurately classifying land cover types in terms of dice coefficient and an average accuracy of 82.30% of 87.81% respectively.