LAUSR

Mountain roads are a source of important basic geographic data used in various fields. The automatic extraction of road images through high-resolution remote sensing imagery using deep learning has attracted considerable attention. But the interference of context information limited extraction accuracy, especially for roads in mountain area. Furthermore, when pursuing research in a new district, many algorithms are difficult to train due to a lack of data. To address these issues, a framework based on remote sensing road-scene neighborhood probability enhancement and improved conditional generative adversarial network (NIGAN) is proposed in this article. This framework can be divided into two sections: 1) road scenes classification section. A remote sensing road-scene neighborhood confidence enhancement method was designed for classifying road scenes of the study area to reduce the impact of nonroad information on subsequent fine-road segmentation and 2) fine-road segmentation section. An improved dilated convolution module, which is helpful in extracting small objects such as road, was added into the conditional generative adversarial network (CGAN) to increase the receptive field and pay attention to global information, and segment roads from the results of road scenes classification section. To validate the NIGAN framework, new mountain road-scene and label datasets were constructed, and diverse comparison experiments were performed. The results indicate that the NIGAN framework can improve the integrity and accuracy of mountain road-scene extraction in diverse and complex conditions. The results further confirm the validity of the NIGAN framework in small samples. In addition, the mountain road-scene datasets can serve as benchmark datasets for studying mountain road extraction.