Semantic Segmentation of Satellite Images Using Deep-Unet

The ability to extract roads, detect buildings, and identify land cover types from satellite images is critical for sustainable development, agriculture, forestry, urban planning, and climate change research. Semantic segmentation with satellite images to extract vegetation covers and urban planning is essential for sustainable development and is a need for the hour. In this paper, Deep Unet, the modified version of Unet, is used for semantic segmentation with pre-processing of the image using FAAGKFCM and SLIC Superpixel to establish mapping for classifying different landfills based on satellite imagery. The research aims to train and test convolutional models for mapping land cover and testing the usability of land cover and identification of changes in land cover. Using mIoU and global accuracy as the evaluation metrics, the proposed model is compared with other methods, namely SegNet, UNet, DeepUNet. It is found that the proposed model outperforms other methods with mIoU of 89.51 and 90.6% global accuracy.