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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Novel Approach to the Unsupervised Extraction of Reliable Training Samples From Thematic Products
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A Novel Approach to the Unsupervised Extraction of Reliable Training Samples From Thematic Products

机译：一种小型方法，无监督的专题产品的可靠培训样本的提取

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摘要

Supervised classification algorithms require a sufficiently large set of representative training samples to generate accurate land-cover maps. Collecting reference data is difficult, expensive, and unfeasible at the large scale. To solve this problem, this article introduces a novel approach that aims to extract reliable labeled data from existing thematic products. Although these products represent a potentially useful information source, their use is not straightforward. They are not completely reliable since they may present classification errors. They are typically aggregated at polygon level, where polygons do not necessarily correspond to homogeneous areas. Finally, usually, there is a semantic gap between map legends and remote sensing (RS) data. In this context, we propose an approach that aims to: 1) perform a domain understanding to detect the discrepancies between the thematic map domain and the RS data domain; 2) use RS data contemporary to the map to decompose the thematic product from the semantic and spatial viewpoints; and 3) extract a database of informative and reliable training samples. The database of weak labeled units is used for training an ensemble of classifiers on recent data whose results are then combined in a majority voting rule. Two sets of experimental results obtained on MS images by extracting training samples from a crop type map and the 2018 Corine Land Cover (CLC) map, respectively, confirm the effectiveness of the proposed approach.
机译：监督分类算法需要足够大的代表性训练样本来产生准确的陆地覆盖图。收集参考数据是困难，昂贵，在大规模中不可行的。为了解决这个问题，本文介绍了一种新的方法，旨在从现有的专题产品中提取可靠的标记数据。虽然这些产品代表了一个有用的信息来源，但它们的使用并不简单。由于它们可能存在分类错误，因此它们并不完全可靠。它们通常在多边形水平处聚合，其中多边形不一定对应于均匀区域。最后，通常，地图图例和遥感（RS）数据之间存在语义差距。在这种情况下，我们提出了一种旨在：1）执行域的理解以检测主题地图域和RS数据域之间的差异; 2）使用当代的RS数据到地图中，从语义和空间观点分解主题产品; 3）提取信息和可靠的培训样本数据库。弱标记单元数据库用于培训近期数据上分类器的集合，其结果在大多数投票规则中组合。通过从作物类型地图和2018冠覆盖（CLC）地图中提取培训样本，在MS图像上获得两组实验结果，分别证实了所提出的方法的有效性。

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