MOA-Net: Multilevel Object Aware Network for Remote Sensing Image Semantic Segmentation

Remote sensing image semantic segmentation is an essential aspect of the intelligent analysis of remote sensing, extensively applied in urban planning, economic assessment, and disaster monitoring. However, the expansive field of view and intricate backgrounds in remote sensing images cause numerous objects of varying sizes and categories to coexist. This results in incomplete object segmentation and poses challenges in restoring the spatial distribution of objects. In this paper, we present a Multilevel Object Aware Network (MOA-Net), which addresses the challenges of remote sensing image semantic segmentation. To be specific, this method is designed from three perspectives. Firstly, we establish a Progressive Multiscale Global-Local Decoder (PMGLD), integrating global-local context information of objects at varying scales through a progressive convolution strategy. Secondly, the Orientation-Aware Attention Mechanism (OAAM) provides orientation information and guides the restoration of interclass 2-D spatial relationships. Finally, obtaining fine-grained features through CNN Stem improves edge segmentation. Experimental outcomes on the Potsdam and Vaihingen datasets indicate that our method of performance and efficiency surpass those of existing methods.