ACR2UNet: Semantic Segmentation of Remotely Sensed Images using Residual-Recurrent UNet and Asymmetric Convolutions

Abstract

Land-use and land-cover (LULC) mapping is one of the significant components in environmental monitoring. LULC mapping, necessary to manage the vital resource of land, has been achieved, in recent years, by segmenting remotely sensed images (RSIs). A standard paradigm for segmentation is UNet, and this paper proposes a novel asymmetric convolutional residualrecurrent UNet architecture, which utilizes the power of asymmetric convolutions as well as residual and recurrent techniques for mapping RSIs. The proposed methodology has a couple of additional advantages. First, asymmetric convolution operations strengthen the square kernels and enhance the semantic feature space. Further, a recurrent network assists in providing rich local contextual information with the help of residual inputs. The presented model is evaluated on the WHDLD dataset for LULC segmentation and is found to achieve an improvement of 1-2% in the mIoU score compared to state-of-the-art methods. © 2023 IEEE.