UAV Remote-Sensing Image Semantic Segmentation Strategy Based on Thermal Infrared and Multispectral Image Features

The availability of high-resolution imagery resources for semantic segmentation research has expanded significantly due to the rapid development of remote-sensing technology utilizing unmanned aerial vehicles (UAVs). These images provide researchers with a more accurate view of the region of interest and allow for more detailed analysis and interpretation of the images. However, semantic segmentation based on UAV remote-sensing imagery still faces new challenges in deriving ground objects. In contrast to the commonly used multispectral (MS) imagery, thermal infrared (TIR) imagery can record the emission of ground objects, making the temperature characteristics of TIR imagery and the color characteristics of MS imagery complementary. These two approaches can be used synergistically to provide more comprehensive image information. On this basis, we propose a strategy for semantic segmentation of UAV images by utilizing both TIR and MS image features. The approach combines principal component analysis (PCA) transformation with a deep learning semantic segmentation network, namely, Deeplv3. The effectiveness of the proposed strategy is evaluated by comparing it with both traditional supervised classification algorithms and deep learning algorithms. According to the results, the proposed strategy exhibits greater robustness, achieving a mean pixel accuracy (MPA) of 92.8% and a mean intersection over union (MIOU) of 73.5%. These results outperform several classical deep learning semantic segmentation algorithms that were also evaluated. The proposed strategy would be beneficial to promote the development of semantic segmentation technology for UAV remote-sensing images.