目前通过合成孔径雷达干涉测量(Interferometric Synthetic Aperture Radar, InSAR)检测开采沉陷盆地主要依靠地下开采资料或人为目视解译，针对这一问题，提出一种针对大范围InSAR干涉图的开采沉陷盆地检测网络(Deformation Basin Detection Network, DBD-Net)；同时，为了训练该网络，利用矿区的真实差分干涉图数据和模拟干涉数据建立了开采沉陷盆地样本库，在神东矿区和兖州矿区各选取3幅不同时间基线的差分干涉影像对网络性能进行验证。结果表明：DBD-Net在大范围InSAR干涉图中对开采沉陷盆地的平均检测准确度为81.87%，漏检和误检区域大多是噪声严重污染和特征不明显的区域。

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.