InSAR mining subsidence basin detection method based on DBD-Net

Abstract

At present, the detection of mining subsidence basins by interferometric synthetic aperture radar (InSAR) mainly relies on underground mining data or human visual interpretation. To solve this problem, this paper proposes a deformation basin detection network (DBD-Net) for large-scale InSAR interferograms. At the same time, in order to train the network, a sample database of mining subsidence basins is established by using real differential interferogram data and simulated interferogram data. In Shendong Mining Area and Yanzhou Mining Area, three differential interference images with different time baselines were selected to verify the network performance. The results show that the detection accuracy of deformation basin detection network (DBD-Net) in large-scale InSAR interferograms for mining subsidence basins is 81.87%. Most of the missed and false detection areas are areas with serious noise pollution and unclear characteristics.

Keywords

• insar
• convolutional neural network
• mining subsidence
• deformation detection
• dbd-net