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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Novel Wrapper Approach for Feature Selection in Object-Based Image Classification Using Polygon-Based Cross-Validation
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A Novel Wrapper Approach for Feature Selection in Object-Based Image Classification Using Polygon-Based Cross-Validation

机译：基于多边形交叉验证的基于对象的图像分类中特征选择的新包装方法

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摘要

Feature selection is becoming a major component of object-based classification as numerous features of segmented object become available. Although common feature selection methods in object-based classification are acknowledged, wrapper-based methods remain an issue due to the diversity of accuracy assessment methods. This letter presents a new wrapper approach using polygon-based cross validation (CV) to overcome possible bias of object-based accuracy assessment for object-based classification. The new method is a two-step wrapper-based feature selection that involves the integration of: 1) feature importance rank using gain ratio and 2) feature subset evaluation using a polygon-based tenfold CV within a support vector machine (SVM) classifier. Several high-resolution images, including both unmanned aerial vehicle images and ISPRS (International Society for Photogrammetry and Remote Sensing) benchmark test data, were used to test the proposed method. Results show that, with the proposed polygon-based CV SVM wrapper, the mean overall accuracy is significantly higher than with an object-based CV SVM wrapper. Furthermore, the proposed method shows potential for comprehensively considering all types of features instead of only spectral features.
机译：随着分段对象的众多功能变得可用，特征选择正成为基于对象分类的主要组成部分。尽管公认了基于对象的分类中的常见特征选择方法，但是由于准确性评估方法的多样性，基于包装器的方法仍然是一个问题。这封信提出了一种使用基于多边形的交叉验证（CV）的新包装方法，以克服基于对象的分类的基于对象的准确性评估的可能偏差。新方法是基于两步包装的特征选择，涉及以下方面的集成：1）使用增益比的特征重要性等级； 2）在支持向量机（SVM）分类器中使用基于多边形的十倍CV进行特征子集评估。几种高分辨率图像，包括无人飞行器图像和ISPRS（国际摄影测量与遥感学会）基准测试数据，均用于测试该方法。结果表明，与所提出的基于多边形的CV SVM包装器相比，平均总体准确性显着高于基于对象的CV SVM包装器。此外，所提出的方法显示出潜在地综合考虑所有类型的特征而不仅仅是光谱特征的潜力。

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  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2017年第3期|409-413|共5页
  • 作者

    Lei Ma; Manchun Li; Yu Gao; Tan Chen; Xiaoxue Ma; Lean Qu;

  • 作者单位

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, China;

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, China;

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, China;

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, China;

    Urban and Resources Environmental College, Nanjing Second Normal University, Nanjing, China;

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Support vector machines; Shape; Training; Image resolution; Computational efficiency; Green products; Unmanned aerial vehicles;

    机译：支持向量机;形状;训练;图像分辨率;计算效率;绿色产品;无人机;

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