| Autor: |
Mingwei Wang, Zhaoqiang Huang, Xinyu Zhang, Yalong Zhang, Maolin Chen |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
International Journal of Applied Earth Observations and Geoinformation, Vol 102, Iss , Pp 102409- (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1569-8432 |
| DOI: |
10.1016/j.jag.2021.102409 |
| Popis: |
Mineral mapping is an important procedure for the utilization of mineral resources, and it is also significant to the analysis of mineralization zone especially for altered minerals. The emergence of remote sensing, especially hyperspectral data has become a new approach for mineral mapping on a wide scale. In addition, spectral angle mapping (SAM) is a commonly used classifier to distinguish the minerals, but the discrimination ability is weak on a mapping scale, and the identification accuracy is poor for a series of minerals with similar spectral curves when a single classifier is applied. In this work, altered minerals are identified at Dehua-Youxi-Yongtai Ore District uniting ground and airborne hyperspectral data, and wavelet SAM (WSAM) tri-training model is constructed to discriminate the category of 9 altered minerals. Experimental results demonstrate that the proposed technique provides the identification accuracy of 82% and 70% for virtual and XRD verifications, and the mapping result is believable compared with measured sampling. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full Text from ScienceDirect