Change Detection of Open-Pit Mine Based on Siamese Multiscale Network

Abstract

Automatic change detection of open-pit mines from high-resolution remote sensing images is of great significance for the mining and management of mineral resources. For this purpose, we propose a siamese multiscale change detection network (SMCDNet) with an encoder-decoder structure. First, the multiscale low-level and high-level features of the bi-temporal image are extracted by a siamese network. Second, a multilevel feature absolute difference (MFAD) module is proposed to fuse the low-level and high-level change features. Finally, convolution and up-sampling operations are used to recover the details of the changed areas. A self-made open-pit mine change detection (OMCD) dataset is employed to conduct experiments. Experimental results have demonstrated that the proposed method is superior to the comparison networks. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> - score of 88.13% is achieved by the proposed SMCDNet. The OMCD dataset produced in this study has been made public at the following link: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://figshare.com/s/ae4e8c808b67543d41e9</uri> .