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：

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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。