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Selection of spectral features for land cover type classification

机译：土地覆盖类型分类的光谱特征选择

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Sophisticated sensors of satellites help researchers collect detailed maps of land surface in various image wavebands. These wavebands are processed to form spectral features identifying distinct land structures. However, depending on the structures subject to the research topic, only a portion of collected features might be sufficient for identification. Aim of this study is to present a scheme to pick most valuable spectral features derived from ASTER imagery in order to distinguish four types of tree ensembles: 'Sugi' (Japanese Cedar), 'Hinoki' (Japanese Cypress), 'Mixed deciduous', and 'Others'. Forward selection, a type of wrapper techniques, was employed with four types of classifiers in several train/test splits. Final rank of each feature was determined by Condorcet ranking after application of each classifier. Results showed that among four classifiers, artificial neural networks helped the selection process choose the most valuable features and a high accuracy value of 90.42% (with a true skill statistics score of 91.26%) was obtained using only top-ten features. For feature sets in smaller sizes, support vector machines classifier also performed well and provided an accuracy of 80.33% (with a true skill statistics score of 81.84%) using only top-three features. With help of these findings, landscape data can be represented in smaller forms with spectral features having most discriminative power. This will help reduce processing time and storage needs of expert systems. (C) 2018 Elsevier Ltd. All rights reserved.
机译：先进的卫星传感器可帮助研究人员收集各种图像波段中陆地表面的详细地图。对这些波段进行处理，以形成识别不同陆地结构的光谱特征。但是，取决于研究主题的结构，仅一部分收集的特征可能足以识别。这项研究的目的是提出一种方案，以选择从ASTER影像中获得的最有价值的光谱特征，以区分四种类型的树木合奏：“杉”（日本雪松），“ H木”（日本柏树），“混合落叶”，和别的'。前向选择是一种包装技术，在几种训练/测试分割中与四种类型的分类器一起使用。应用每个分类器后，通过Condorcet排名确定每个功能的最终排名。结果表明，在四个分类器中，人工神经网络帮助选择过程选择了最有价值的特征，仅使用前十个特征就获得了90.42％的高精度值（真实技能统计得分为91.26％）。对于较小尺寸的特征集，支持向量机分类器也表现良好，仅使用前三个特征就可提供80.33％的准确率（真实技能统计得分为81.84％）。借助这些发现，可以以具有最大判别力的光谱特征以较小的形式表示景观数据。这将有助于减少专家系统的处理时间和存储需求。 （C）2018 Elsevier Ltd.保留所有权利。

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