A multimodal semantic segmentation for airport runway delineation in panchromatic remote sensing images

Monitoring airport runways in panchromatic remote sensing images is helpful for both civil and strategic communities in effective utilization of the large-area acquisitions. This paper proposes a novel multimodal semantic segmentation approach for effective delineation of the runways in panchromatic remote sensing images. The proposed approach aims to learn complementary information from two modalities, namely, panchromatic image and digital elevation model (DEM) to obtain discriminative features of the runway. The fusion of image features and the corresponding terrain information is performed by stacking the image and DEM by leveraging the merits of both Transformers and U-Net architecture. We perform the experiments on Cartosat-1 panchromatic satellite images with the corresponding Cartosat-1 DEM scenes. The experimental results demonstrate a significant contribution of terrain information to the segmentation process in achieving the contours of airport runways effectively.