An improved fully convolution network model for change detection in mining areas using sentinel-2 images

High-resolution satellite images have recently been widely used for change detection of mineral resources and mining area environments. However, with their increasing resolution and number of feature sub-categories, traditional machine learning methods have become sensitive to pseudo-changes and intra-class changes, which are prone to producing misclassifications. To address these problems, a novel fully convolutional network, the Hybrid Dilated Convolutional Siamese Network (HDC-Siam) was proposed. This model combines the modular dilated convolution network with the fully convolutional Siamese networks structure, in order to reduce the commission rate. In this paper, pairs of Sentinel-2 images with an interval of about two years was used as the experimental data. The HDC-Siam model was used to detect changes, where we evaluated the accuracy in the Dongsheng coalfield in Ordos, China and the Kuznetsk coalfield in Kemerovo, Russia. We obtained F (1)-scores of 85% and 75% for these respective locations. In addition, we conducted comparative experiments using two other methods - Fully Convolutional Siamese - Difference (FC-Siam-diff) and Fully Convolutional Early Fusion (FC-EF) - in order to verify that the HDC-Siam works.