Open-pit mine change detection from high resolution remote sensing images using DA-UNet++ and object-based approach

Automatic extraction of land use and land cover changes in mining areas is crucial for the supervision of mineral resources. This study proposes an automatic open-pit mine (OM) change detection method via a novel deep learning model, DA-UNet++ (Deformable-Attention-UNet++), and an object-based approach from high resolution remote sensing images. First, by embedding a deformable convolution module and attention mechanism module into UNet++ network, and constructing a skip sampling strategy, DA-UNet++ is proposed to combat the high heterogeneity of shape and scale of ground objects in OM environment and improve the change detection accuracy. Second, an object-based post-processing (OBPP) approach is presented for enhancing raw change detection results of DA-UNet++, which includes object-based expectation maximisation optimisation and morphological close operation. In the experiments, Season-varying Change Detection Dataset is first employed to evaluate the effectiveness of the proposed DA-UNet++ by comparing with other benchmark models. Then, self-made Ordos OM Change Detection Dataset including 38 OMs is utilised to verify the efficacy of the proposed method, and comprehensive metric F1−score of 85.6% is achieved. Evaluation results prove that the proposed method provides superior change detection performance compared with several benchmark methods, and has practical significance for OM management and monitoring.