当前位置: X-MOL 学术J. Spat. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Application of unsupervised learning of finite mixture models in ASTER VNIR data-driven land use classification
Journal of Spatial Science ( IF 1.0 ) Pub Date : 2019-03-01 , DOI: 10.1080/14498596.2019.1570478
Bo Zhao 1 , Fan Yang 2, 3, 4 , Rongzhen Zhang 5 , Junping Shen 1 , Jürgen Pilz 6 , Dehui Zhang 7
Affiliation  

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.

中文翻译:

有限混合模型无监督学习在ASTER VNIR数据驱动的土地利用分类中的应用

摘要 基于 ASTER VNIR 图像,我们研究了 MML-EM(最小消息长度准则-期望最大化)算法在奥地利南部土地利用分类中的适用性。首先,利用RVI(比率植被指数)和PC1(第一主成分)波段来增强目标信息;其次,将MML-EM算法和基于地形分析的图像裁剪联合用于表面类型判别。研究结果表明,MML-EM 方法可以提供精细的图像分类结果,这是它首次应用于该领域。
更新日期:2019-03-01
down
wechat
bug