Remote Sensing Image Road Segmentation Based on Conditions Perceived 3D UX-Net

Abstract The rapid development of remote sensing technology has broadened channels for people to access information, making information extraction from remote sensing images an essential means of acquiring data. However, factors such as shadows, occlusions and spectral similarities between roads and other objects contribute to high mis-segmentation rates in existing deep learning semantic segmentation networks when segmenting roads in remote sensing images. Based on this, this paper proposes an improved version of the DDSA(Data-associated Deep Supervision Attention) network based on the three-dimensional user experience module for road segmentation. Simultaneously, an attention mechanism is integrated into the network to better focus on the crucial features of the input data and capture regions of interest. Finally, by adding deeper levels of supervision signals to different levels of the neural network, the network pays attention to different features of the input data, guiding the network to learn more discriminative features at each level, which effectively addresses the issue of poor performance in predicting unknown data. Experimental results demonstrate that, the DDSA UX Net(User Experience Network) exhibits outstanding performance in various metric scores and segmentation quality. These results support the potential of the DDSA UX Net network in applications such as urban development monitoring, traffic infrastructure planning and disaster management.