| Application of unsupervised learning of finite mixture models in ASTER VNIR data-driven land use classification | |
| Bo Zhao; Fan Yang; Rongzhen Zhang; Junping Shen; Jürgen Pilz; Dehui Zhang | |
| 2019 | |
| Source Publication | Journal of Spatial Science
 |
| Pages | 1–24 |
| Abstract | Based on an ASTER VNIR image, we studied the applicability of the MML-EM (Minimum Message Length Criterion-Expectation Maximization) algorithm for land-use classification in southern Austria. Firstly, the RVI (ratio vegetation index) and PC1 (first principal component) bands have been utilized to enhance the targeted information; secondly, the MML-EM algorithm and the terrain analysis-based imagery clipping were jointly used for surface type discrimination. Findings showed that the MML-EM method can provide refined imagery classification results and this is the first time it has been applied in this realm. |
| Keyword | Mixture Gaussian Distribution land Use topographical Analysis remote Sensing |
| Indexed By | SCI |
| Language | 英语 |
| Document Type | 期刊论文 |
| Identifier | http://ir.gyig.ac.cn/handle/42920512-1/10944 |
| Collection | 矿床地球化学国家重点实验室 |
| Affiliation | 1.Advanced Algorithm Research Division, Beijing PIESAT Information Technology Co., Ltd, Beijing, China 2.Beijing Institute of Geology for Mineral Resources, Beijing, China 3.Key Laboratory of Geochemical Cycling of Carbon and Mercury in the Earth’s Critical Zone, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang, China 4.State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, China 5.Henan Institute of Geological Survey, Zhengzhou, China 6.Institute of Statistics, Alpen-Adria-Universität Klagenfurt, Klagenfurt, Austria 7.School of Earth Sciences and Resources, China University of Geosciences, Beijing, China |
| Recommended Citation GB/T 7714 | Bo Zhao；Fan Yang；Rongzhen Zhang；Junping Shen；Jürgen Pilz；Dehui Zhang. Application of unsupervised learning of finite mixture models in ASTER VNIR data-driven land use classification[J]. Journal of Spatial Science,2019:1–24. |
| APA | Bo Zhao；Fan Yang；Rongzhen Zhang；Junping Shen；Jürgen Pilz；Dehui Zhang.(2019).Application of unsupervised learning of finite mixture models in ASTER VNIR data-driven land use classification.Journal of Spatial Science,1–24. |
| MLA | Bo Zhao；Fan Yang；Rongzhen Zhang；Junping Shen；Jürgen Pilz；Dehui Zhang."Application of unsupervised learning of finite mixture models in ASTER VNIR data-driven land use classification".Journal of Spatial Science (2019):1–24. |
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| Application of unsup（4880KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
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