Enhancing Aerial Semantic Segmentation With Feature Aggregation Network for DeepLabV3+

As a cutting-edge deep encoder-decoder architecture, DeepLabV3+ has been realized as a cutting-edge solution for image segmentation, especially aerial semantic segmentation in remote sensing applications. This is because of an atrous spatial pyramid pooling (ASPP) block deployed in its encoder with multiple atrous convolutional layers to enrich diversified feature extraction and learning efficiency. However, the DeepLabV3+ encoder-decoder architecture has some limitations, including the lack of information during the upsampling process and some inappropriate customizations that cause incorrect segmentation. To address these shortcomings, we introduce an efficient architecture with a novel feature aggregation network (FAN), which facilitates the extraction of features across multiple scales and stages. Concurrently, we apply some adaptive upgrades to the ASPP block, involving a new set of dilation factors that are adept at accommodating low-resolution inputs. Through simulations, we demonstrate the effectiveness and generalizability of our improved model by evaluating it with different backbones and dilation rates. In addition, compared with recent deep segmentation models, our improved model is superior in terms of mean
$F1$
score (mF1) by at least 1.32%–6.34% and 0.98%–5.61% on the UAVid and Vaihingen datasets, respectively.