Open-pit mine change detection from high resolution remote sensing images using DA-UNet++ and object-based approach
Shouhang Du, Wei Li, Jun Li, Shihong Du, Chengye Zhang, Yaqin Sun
Abstract
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.