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Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed

机译:高光谱影像与航空摄影和多光谱影像在扫帚snake草作图方面的比较

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摘要

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a range-land area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.
机译:扫帚snake草(Gutierrezia sarothrae(Pursh)Britt。&Rusby)是北美西部最广泛和丰富的牧场杂草之一。这项研究的目的是评估机载高光谱图像,并将其与航空彩色红外(CIR)摄影和多光谱数字图像进行比较,以绘制扫帚蛇草侵扰的地图。机载高光谱图像以及航空CIR照片和数字CIR图像是从得克萨斯州南部的一个陆域地区获取的。使用最小噪声分数(MNF)变换高光谱图像,然后使用最小距离,马氏距离,最大似然度和光谱角映射器(SAM)分类器进行分类。将数字化的航空照片和数字图像分别镶嵌为一张照片图像和一张数字图像。然后使用相同的分类器对它们进行分类。准确性评估表明,最大似然分类器对三种类型的图像表现最佳。对于CIR摄影图像,数字CIR图像和经MNF转换的高光谱图像,三类分类图(蛇皮草,混合木本和混合草皮)的最佳总体精度分别为91.0%,92.5%和95.0%。 Kappa分析表明,在三种类型的图像之间,基于最大似然的分类没有显着差异。这些结果表明,机载高光谱图像以及航空摄影和多光谱图像可用于监视和绘制牧场上的扫帚蛇草侵扰。

著录项

  • 来源
    《International journal of remote sensing》 |2010年第20期|p.5423-5438|共16页
  • 作者

    CHENGHAI YANG; J. H. EVERITT;

  • 作者单位

    USDA-ARS Kika de la Garza Subtropical Agricultural Research Center, 2413 E. Highway 83, Weslaco, TX 78596, USA;

    USDA-ARS Kika de la Garza Subtropical Agricultural Research Center, 2413 E. Highway 83, Weslaco, TX 78596, 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
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 13:25:31

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