DIResUNet: Architecture for multiclass semantic segmentation of high resolution remote sensing imagery data

Scene understanding is an important task in information extraction from high-resolution aerial images, an operation which is often involved in remote sensing applications. Recently, semantic segmentation using deep learning has become an important method to achieve state-of-the-art performance in pixel-level classification of objects. This latter is still a challenging task due to large pixel variance within classes possibly coupled with small pixel variance between classes. This paper proposes an artificial-intelligence (AI)-based approach to this problem, by designing the DIResUNet deep learning model. The model is built by integrating the inception module, a modified residual block, and a dense global spatial pyramid pooling (DGSPP) module, in combination with the well-known U-Net scheme. The modified residual blocks and the inception module extract multi-level features, whereas DGSPP extracts contextual intelligence. In this way, both local and global information about the scene are extracted in parallel using dedicated processing structures, resulting in a more effective overall approach. The performance of the proposed DIResUNet model is evaluated on the Landcover and WHDLD high resolution remote sensing (HRRS) datasets. We compared DIResUNet performance with recent benchmark models such as U-Net, UNet++, Attention UNet, FPN, UNet+SPP, and DGRNet to prove the effectiveness of our proposed model. Results show that the proposed DIResUNet model outperforms benchmark models on two HRRS datasets