LOANet: a lightweight network using object attention for extracting buildings and roads from UAV aerial remote sensing images.

ABSTRACT: Semantic segmentation for extracting buildings and roads from uncrewed aerial vehicle (UAV) remote sensing images by deep learning becomes a more efficient and convenient method than traditional manual segmentation in surveying and mapping fields. In order to make the model lightweight and improve the model accuracy, a lightweight network using object attention (LOANet) for buildings and roads from UAV aerial remote sensing images is proposed. The proposed network adopts an encoder-decoder architecture in which a lightweight densely connected network (LDCNet) is developed as the encoder. In the decoder part, the dual multi-scale context modules which consist of the atrous spatial pyramid pooling module (ASPP) and the object attention module (OAM) are designed to capture more context inform

SUBMITTER: Han X 

PROVIDER: S-EPMC10403170 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature