Volume 41 Issue 3
May  2022
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Tang Shulan. Aster geological mapping based on multi-scale texture classification and mineral recognition[J]. Bulletin of Geological Science and Technology, 2022, 41(3): 311-320. doi: 10.19509/j.cnki.dzkq.2022.0077
Citation: Tang Shulan. Aster geological mapping based on multi-scale texture classification and mineral recognition[J]. Bulletin of Geological Science and Technology, 2022, 41(3): 311-320. doi: 10.19509/j.cnki.dzkq.2022.0077

Aster geological mapping based on multi-scale texture classification and mineral recognition

doi: 10.19509/j.cnki.dzkq.2022.0077
  • Received Date: 11 Dec 2021
  • Remote sensing technology has become an indispensable means in geological survey. In order to improve the efficiency and accuracy of geological mapping, a method based on Aster automatic lithology classification combined with the identification of main rock forming minerals is proposed in this study. Firstly, the principal component transform of ASTER data is carried out, the first principal component is selected for multi-scale Haar wavelet decomposition, and the statistical characteristics of wavelet coefficients are taken as texture features to construct multi-dimensional feature space of texture and spectrum; Then, support vector machine is adopted to classify lithology; At the same time, the main rock forming minerals are extracted according to the spectral characteristics; Finally, the main rock forming minerals are superimposed on the classification results, and the lithology mapping is completed in combination with the field investigation background. The confusion matrix results show that the classification accuracy of spectrum- wavelet texture can reach 83.496 2%, which is 2.675 6% higher than that of spectrum-gray level co-occurrence matrix texture classification and 6.3189% higher than that of spectral feature classification. Compared with the maximum likelihood classification method, the classification accuracy of SVM is improved by 6.623 7%. The mineral extraction results indicate that the extraction index of structure can effectively extract muscovite, biotite, calcite, amphibole and other minerals. It can be seen that image processing technology, machine learning algorithm and band operation can be used as effective means of remote sensing mapping in areas with less vegetation coverage.


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