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.