科研之友
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摘要
This paper proposes a new morphological mining feature index (MMFI) by synthesizing multi-scale and multi-direction differential morphological profiles (DMPs) to effectively separate REMAs from other land covers with similar spectral signals and local brightness contrast. The MMFI enhances the local brightness contrast of rare earth mining areas (REMAs) by highlighting the morphological characteristics of REMA structure, and improves the identification of roads and bare soil, which have similar spectral signatures to REMAs. Moreover, a new threshold optimization method that maximizes the histogram entropy is presented, whereby REMAs can be automatically extracted from the MMFI image without sample collection and machine learning. Therefore, it is a fully automatic method suitable for REMA extraction over large areas. To validate the proposed method, three temporal Landsat images acquired of Changting County, China, were used to extract REMA information. Our results demonstrate that the proposed method can achieve good classification accuracy compared with other methods.
