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首页> 外文期刊>International journal of remote sensing >Detection and classification of invasive saltcedar through high spatial resolution airborne hyperspectral imagery
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Detection and classification of invasive saltcedar through high spatial resolution airborne hyperspectral imagery

机译：通过高空间分辨率机载高光谱图像对入侵性柳杉进行检测和分类

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摘要

We evaluated the performance of airborne HyperSpecTIR (HST) images for detecting and classifying the invasive riparian vegetation saltcedar along the Muddy River in Clark County, Nevada. HyperSpecTIR image reflectance spectra (227 bands, 450-2450 nm) were acquired for the following four vegetation covers: invasive saltcedar, native honey mesquite, grassland patches and crops. We compared five feature reduction approaches: band selection based on Jeffreys-Matusita distance, principal component analysis (PCA), minimum noise fraction (MNF), segmented principal component transform (SPCT) and segmented minimum noise fraction (SMNF). In addition, maximum likelihood (ML) and two spectral angle mapper (SAM) classifiers were applied to all extracted bands or features. Classification accuracies were compared among all classification approaches. Although the overall accuracy of maximal likelihood classifiers generally surpassed that of SAM classifiers, the highest overall accuracy was achieved by a SMNF-SAM combination with adjusted angular thresholds for classes. We concluded that high spectral and spatial resolution imagery can be used to detect and classify invasive saltcedar in this arid area.
机译：我们评估了机载HyperSpecTIR（HST）图像在检测和分类内华达州克拉克县泥泞河沿岸侵入性河岸植被盐杉的性能。为以下四个植被覆盖层获取了HyperSpecTIR图像反射光谱（227个波段，450-2450 nm）：入侵性柳杉，本地蜂蜜豆科灌木，草原斑块和农作物。我们比较了五种特征减少方法：基于Jeffreys-Matusita距离的频带选择，主成分分析（PCA），最小噪声分数（MNF），分段主成分变换（SPCT）和分段最小噪声分数（SMNF）。此外，最大似然（ML）和两个光谱角度映射器（SAM）分类器已应用于所有提取的波段或特征。比较所有分类方法中的分类精度。尽管最大似然分类器的总体准确性通常超过SAM分类器，但通过SMNF-SAM组合并为类别调整了角度阈值，可以实现最高的总体准确性。我们得出的结论是，高光谱和空间分辨率图像可用于检测和分类该干旱地区的侵袭性柳杉。

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  • 来源
    《International journal of remote sensing》 |2011年第8期|p.2131-2150|共20页
  • 作者

    XIN MIAO; ROHIT PATIL; JILL S. HEATON; RICHARD C. TRACY;

  • 作者单位

    Department of Geography, Geology and Planning, Missouri State University,Springfield, MO 65897, USA;

    Department of Geography, University of Nevada at Reno, NV 89557, USA;

    Department of Geography, University of Nevada at Reno, NV 89557, USA;

    Department of Geography, University of Nevada at Reno, NV 89557, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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