GCN-Based Semantic Segmentation Method for Mine Information Extraction in GAOFEN-1 Imagery

Mine information extraction is of great significance to the construction of ecological civilization, the dynamic monitoring of mine development and the scientific management of mineral resources. With the emergence of high spatial resolution remote sensing imagey, traditional machine learning method gradually cannot meet the increasing demands of image interpretation. CNN-based semantic segmentation method provides a great solution for this issue. With the deepening of network layers, more the high-level features can be obtained, which brings the outstanding performance of many computer vision tasks, but also leads to the loss of structural information, which is crucial for mine information extraction. Therefore, in order to improve these drawbacks, we proposed a novel network based on the classical semantic segmentation network, SegNet, and Graph Convolutional Network (GCN) that makes our method more sensitive to structural information. Then, taking the iron mine located in Qian'an City, Hebei Province as experimental area, we employed our method to extract five mainly mine objects: stopes, ore heap, waste dump, tailings reservoir and concentration based on GF-1 imagery. Compared with SegNet, the mIoU of our method was improved by about 5% on our dataset and was improved by about 2.2% on PASCAL VOC2012 dataset.