Object-Oriented Extraction of Land Occupation Types in Mining Areas by Using Densenet

The acquisition of information on land occupation in mining areas is important for ecological management and restoration of mining areas. The current work has the problem of low extraction accuracy due to the small number of typical land category samples and simple network structure. In order to improve the accuracy of extraction of land use information in mining areas, this paper uses the data from the Gaofen-2 satellite, combines the dense connected neural network (DenseNet) with object-oriented ideas to extract mining areas in Yuzhou City, Henan Province. The results are compared with convolutional neural network (CNN) and support vector machine (SVM). The results show that the overall accuracy and kappa coefficient of the proposed DenseNet is 87.46% and 0.84, which are 0.06%, 2.63% and 0.004, 0.039 higher than that of the CNN and SVM respectively. The proposed DenseNet performs the best in the extraction of open-pit and waste-dump area, which indicating that it can provide promising advantages in mine information extraction, and can provide technical support for environmental monitoring and scientific management of mining areas.