Abstract
Traditional remote sensing extraction of open-pit mining area usually has low efficiency. To reduce time of extraction, taking laterite nickel as an example, we proposed a remote sensing extraction strategy of ground objects information in open-pit mining area. Firstly, the optimal segmentation scale parameter (SP) of each category was selected based on the local variance change rate. Secondly, the spectral feature, spatial feature and inter-object relationship feature of high spatial resolution remote sensing images were taken full advantage of feature space construction. Finally, based on the selected SPs and feature space, a top-down hierarchical classification framework was established. The overall accuracy and the Kappa coefficient of the proposed method were 95.49% and 0.9387 by the confusion matrix test, respectively. Compared with the multiple experiments to determine segmentation scales, the proposed method improves the efficiency, meanwhile guarantees the accuracy.
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