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首页> 外文期刊>Geoscience and Remote Sensing Letters, IEEE >Wrapper-Based Feature Subset Selection for Rapid Image Information Mining
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Wrapper-Based Feature Subset Selection for Rapid Image Information Mining

机译：基于包装的特征子集选择，用于快速图像信息挖掘

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摘要

In a disaster, there is a need for rapid image-information retrieval in real or near real time from vast amounts of data coming from multiple remote-sensing sensors. In general, image information mining (IIM) approaches produce enormous amounts of features that are computationally expensive and inefficient to process before the actual information discovery takes place. Also, it is complicated because the combination of the features has little relevance to the hypothesis space. Hence, selecting a relevant subset of features is necessary to overcome these problems and to provide an efficient representation of the target class. In this letter, we propose feature selection and feature transformations based on a wrapper-based genetic algorithm approach. A support vector machine classification is applied for generating predictive models for those land-cover classes that are important in a coastal disaster event. The proposed system, rapid IIM, is a region-based approach where, in lieu of the prevalent pixel-based methods, it localizes interesting zones and enables rapid querying. Results from this study indicate that selecting relevant feature subsets increases the rate of correctly identifying a semantic class and also enables this process with less number of features.
机译：在灾难中，需要从多个遥感传感器来的大量数据中实时或近实时地快速检索图像信息。通常，图像信息挖掘（IIM）方法会产生大量功能，这些功能在实际的信息发现发生之前在计算上昂贵并且处理效率低下。而且，由于特征的组合与假设空间的相关性很小，因此很复杂。因此，选择特征的相关子集对于克服这些问题并提供目标类的有效表示是必要的。在这封信中，我们建议基于基于包装器的遗传算法方法进行特征选择和特征转换。支持向量机分类用于为那些在沿海灾难事件中很重要的土地覆盖类别生成预测模型。所提出的系统，快速IIM，是一种基于区域的方法，代替了常见的基于像素的方法，它可以定位感兴趣的区域并实现快速查询。这项研究的结果表明，选择相关的特征子集可以提高正确识别语义类的速度，并且还可以减少特征数量。

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