Semantic Segmentation of UAV Images Based on Transformer Framework with Context Information

With the advances in Unmanned Aerial Vehicles (UAVs) technology, aerial images with huge variations in the appearance of objects and complex backgrounds have opened a new direction of work for researchers. The task of semantic segmentation becomes more challenging when capturing inherent features in the global and local context for UAV images. In this paper, we proposed a transformer-based encoder-decoder architecture to address this issue for the precise segmentation of UAV images. The inherent feature representation of the UAV images is exploited in the encoder network using a self-attention-based transformer framework to capture long-range global contextual information. A Token Spatial Information Fusion (TSIF) module is proposed to take advantage of a convolution mechanism that can capture local details. It fuses the local contextual details about the neighboring pixels with the encoder network and makes semantically rich feature representations. We proposed a decoder network that processes the output of the encoder network for the final semantic level prediction of each pixel. We demonstrate the effectiveness of this architecture on UAVid and Urban Drone datasets, where we achieved mIoU of 61.93% and 73.65%, respectively.

Automated summary

This paper proposes a Transformer-based encoder-decoder architecture for precise segmentation of UAV images. By utilizing self-attention and convolution mechanisms, the architecture captures global and local contextual information to generate semantically rich feature representations. The effectiveness of the architecture is demonstrated on UAVid and Urban Drone datasets with promising results.