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Benthic mapping of coastal waters using data fusion of hyperspectral imagery and airborne laser bathymetry.

机译：使用高光谱图像和机载激光测深仪的数据融合对沿海水域进行底栖测绘。

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摘要

One goal of mapping, the accurate classification of the object space, can be achieved by visual interpretation or analysis of relevant data. Most mapping of earth features relies on the latter method, and is realized using remote sensing. Various airborne sensors are used today for generating topographic and hydrographic mapping products. In this research, we combined data from airborne hyperspectral imagery and airborne laser bathymetry, using data fusion techniques, to map the benthic environment of coastal waters.; Airborne laser bathymetry (ALB) uses laser pulse return waveforms to estimate water depth. These signals are attenuated by the water depth and clarity. A portion of the waveform signal, the peak bottom return, is a function of the bottom reflectance, and therefore, the bottom type. The purpose of this research is to exploit the peak bottom return signal of ALB to obtain benthic information, and then use the information, in combination with spectral imaging information, to aid in benthic classification.; We used AVIRIS hyperspectral data and SHOALS ALB data, obtained over Kaneohe Bay, Hawaii, for this research. After preprocessing the datasets, the water attenuation effects were removed from the AVIRIS data using a radiative transfer model. A variant of this model, developed for this research, was used on the ALB dataset to correct for water attenuation, resulting in a parameter we defined as pseudoreflectance. We classified the resulting datasets using the Maximum Likelihood supervised classification technique. Accuracy assessments of the classifications showed overall accuracies of 80.2% and 66.9% for the AVIRIS classification and the SHOALS classification, respectively. The two classifications were merged using the Dempster-Shafer (D-S) decision-level data fusion method, using a priori weights from the Maximum Likelihood classifications. The resulting D-S classification had an overall accuracy of 87.2%. For comparison, we classified the AVIRIS data (corrected for water attenuation) combined with a depth channel, producing an overall accuracy of 85.3%. Kappa coefficient analysis of all four classifications resulted in 82% confidence that the Kappa coefficients of the D-S classification and the AVIRIS-plus-depth classification are different. Kappa confidence levels greater than 99% were calculated for all the other pairs of classifications.; The results indicate that ALB pseudoreflectance, computed from the peak bottom return waveform signals, contains information that aids in the benthic mapping process, and can be used in a sensor fusion algorithm with hyperspectral data to achieve greater accuracy in bottom classification. Further research into the computation of bottom reflectance from the ALB bottom return waveform may yield additional improvements.
机译：可以通过视觉解释或相关数据分析来实现映射的目标，即对象空间的准确分类。地球特征的大多数映射都依赖于后一种方法，并且是通过遥感实现的。今天，各种机载传感器用于生成地形图和水文地图产品。在这项研究中，我们使用数据融合技术将机载高光谱图像和机载激光测深仪的数据相结合，以绘制沿海水域底栖环境的地图。机载激光测深仪（ALB）使用激光脉冲返回波形估算水深。这些信号因水深和清晰度而衰减。波形信号的一部分（峰值底部返回）是底部反射率（因此是底部类型）的函数。这项研究的目的是利用ALB的峰谷返回信号获得底栖生物信息，然后将其与光谱成像信息结合使用，以帮助进行底栖生物分类。我们使用从夏威夷卡尼奥赫湾获得的AVIRIS高光谱数据和SHOALS ALB数据进行此项研究。在对数据集进行预处理之后，使用辐射传输模型从AVIRIS数据中删除了水衰减效应。为该研究开发的该模型的变体被用于ALB数据集以校正水衰减，从而导致我们定义为伪反射率的参数。我们使用最大似然监督分类技术对结果数据集进行了分类。分类的准确性评估显示，AVIRIS分类和SHOALS分类的总体准确度分别为80.2％和66.9％。使用Dempster-Shafer（D-S）决策级数据融合方法，使用最大似然分类的先验权重，将这两个分类合并。最终的D-S分类的总体准确度为87.2％。为了进行比较，我们结合深度通道对AVIRIS数据（针对水衰减进行了校正）进行了分类，从而产生了85.3％的整体精度。对所有四个分类的Kappa系数分析得出82％的置信度，即D-S分类和AVIRIS加深度分类的Kappa系数不同。对于所有其他类别的分类，计算出的Kappa置信度大于99％。结果表明，从峰值底部返回波形信号计算得出的ALB伪反射率包含有助于底层映射过程的信息，可用于具有高光谱数据的传感器融合算法中，从而在底部分类中实现更高的准确性。从ALB底部返回波形计算底部反射率的进一步研究可能会产生其他改进。

著录项

  • 作者

    Lee, Mark Patrick.;

  • 作者单位

    University of Florida.;

  • 授予单位 University of Florida.;
  • 学科 Engineering Civil.; Remote Sensing.; Physical Oceanography.
  • 学位 Ph.D.
  • 年度 2003
  • 页码 108 p.
  • 总页数 108
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 建筑科学;遥感技术;海洋物理学;
  • 关键词

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