My PhD thesis focuses on improving semantic segmentation of aerial and satellite images, a crucial task for applications like agriculture planning, map updates, route optimization, and navigation. Current models like the Deep Convolutional Encoder-Decoder (DCED) have limitations in accuracy due to their inability to recover low-level features and the scarcity of training data. To address these issues, I propose a new architecture with five key enhancements, a Global Convolutional Network (GCN) for improved feature extraction, channel attention for selecting discriminative features, domain-specific transfer learning to address data scarcity, Feature Fusion (FF) for capturing low-level details, and Depthwise Atrous Convolution (DA) for refining features. Experiments on Landsat-8 datasets and the ISPRS Vaihingen benchmark showed that my proposed architecture significantly outperforms the baseline models in remote sensing imagery.
