IRON ORE REGION SEGMENTATION USING HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON RES-U-NET

Deep learning has found many applications in high-resolution remote sensing image interpretation. In this study, an image analysis system is presented, consisting of image segmentation and mineral volume change estimation In this system, a revised U-Net structure, called Res-U-Net, is proposed by combining U-Net and residual structure for image segmentation. Experiments are performed on the collected high-resolution remote sensing images, which were annotated with iron ore positive, iron ore negative, and background, and the results demonstrate the superiority of the proposed Res-U-Net over other image segmentation methods. Our proposed Res-U-Net outperforms the traditional U-Net by achieving pixel-wise accuracy of 92% and mean intersection over union (mIOU) 86%, as well as faster frame rate of 35 FPS on test dataset.