Author:
Wu Bo,Fang Chaoyang,Yu Le,Huang Xin,Zhang Qiang
Abstract
This paper proposes a new morphological mining feature index (<small>MMFI</small>) by synthesizing multi-scale and multi-direction differential morphological profiles (DMPs) to effectively separate <small>REMAS</small> from other land covers with similar spectral
signals and local brightness contrast. The <small>MMFI</small> enhances the local brightness contrast of rare earth mining areas (<small>REMAS</small>) by highlighting the morphological characteristics of <small>REMA</small> structure, and improves the identification
of roads and bare soil, which have similar spectral signatures to <small>REMAS</small>. Moreover, a new threshold optimization method that maximizes the histogram entropy is presented, whereby <small>REMAS</small> can be automatically extracted from the <small>MMFI</small>
image without sample collection and machine learning. Therefore, it is a fully automatic method suitable for <small>REMA</small> extraction over large areas. To validate the proposed method, three temporal Landsat images acquired of Changting County, China, were used to extract
<small>REMA</small> information. Our results demonstrate that the proposed method can achieve good classification accuracy compared with other methods.
Publisher
American Society for Photogrammetry and Remote Sensing
Subject
Computers in Earth Sciences
Cited by
7 articles.
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