Light-Weight Semantic Segmentation Network for UAV Remote Sensing Images

Semantic segmentation for unmanned aerial vehicle (UAV) remote sensing images has become one of the research focuses in the field of remote sensing at present, which could accurately analyze the ground objects and their relationships. However, conventional semantic segmentation methods based on deep learning require large-scale models that are not suitable for resource-constrained UAV remote sensing tasks. Therefore, it is important to construct a light-weight semantic segmentation method for UAV remote sensing images. With this motivation, we propose a light-weight neural network model with fewer parameters to solve the problem of semantic segmentation of UAV remote sensing images. The network adopts an encoder-decoder architecture. In the encoder, we build a light-weight convolutional neural network model with fewer channels of each layer to reduce the number of model parameters. Then, feature maps of different scales from the encoder are concatenated together after resizing to carry out the multiscale fusion. Moreover, we employ two attention modules to capture the global semantic information from the context and the correlation among channels in UAV remote sensing images. In the decoder part, the model obtains predictions of each pixel through the softmax function. We conducted experiments on the ISPRS Vaihingen dataset, UAVid dataset, and UDD6 dataset to verify the effectiveness of the light-weight network. Our method obtains quality semantic segmentation results evaluated on UAV remote sensing datasets with only 9 M parameters the model owns, which is competitive among popular methods with the same level of parameters.