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首页> 外文学位 >Development of the Hyperspectral Coastal Image Analysis Toolbox (HyCIAT) with a focus on hyperspectral and lidar data fusion.
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Development of the Hyperspectral Coastal Image Analysis Toolbox (HyCIAT) with a focus on hyperspectral and lidar data fusion.

机译：开发了高光谱海岸图像分析工具箱（HyCIAT），重点是高光谱和激光雷达数据融合。

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摘要

The Hyperspectral Coastal Image Analysis Toolbox integrates algorithms for the estimation of water optical properties, bathymetry and fractional abundances of bottom composition in a graphical interface using Matlab to perform analysis of hyperspectral images of shallow waters. The primary algorithms included in the toolbox were previously developed by students and faculty at LARSIP at University of Puerto Rico at Mayaguez. Work was also performed to add new capabilities to these existing algorithms by incorporating the capacity to fuse lidar data into the hyperspectral processing. The HyCIAT algorithms are fundamentally based on the Lee et al. [1] semi-analytical inversion model combined with linear unmixing techniques developed by Goodman [2] and Castrodad [3]. The Lee algorithm is one of the more commonly used models for the estimation of water optical properties and bathymetry from passive hyperspectral imagery. Goodman integrated the Lee model with unmixing algorithms (LIGU) to first independently derive estimates of water properties and bathymetry, and then derive the habitat composition. Castrodad similarly combined the Lee model with an unmixing algorithm (CIUB), but this derives the water properties, bathymetry and habitat composition simultaneously. Both these techniques are included in HyCIAT as well as new capabilities for both models that allow lidar bathymetry to be used as input. This work presents the HyCIAT toolbox, and evaluates model performance using both simulated data and actual airborne hyperspectral imagery. Results indicate that accuracy in parameter retrieval is increased when the lidar data is included in the models.
机译：高光谱海岸图像分析工具箱使用Matlab在图形界面中集成了用于估算水的光学特性，测深法和底部成分的分数丰度的算法，并使用Matlab进行了浅水区高光谱图像的分析。该工具箱中包含的主要算法以前是由Mayaguez的波多黎各大学的LARSIP的学生和教师开发的。通过整合将激光雷达数据融合到高光谱处理中的功能，还为向这些现有算法添加新功能而开展了工作。 HyCIAT算法基本基于Lee等人的方法。 [1]与Goodman [2]和Castrodad [3]开发的线性分解技术相结合的半解析反演模型。 Lee算法是从被动高光谱图像估算水的光学特性和测深法的最常用模型之一。 Goodman将Lee模型与分解算法（LIGU）集成在一起，首先独立地得出水质和测深的估计值，然后得出生境组成。 Castrodad同样将Lee模型与分解算法（CIUB）结合在一起，但这同时得出了水的性质，测深和栖息地组成。这两种技术都包含在HyCIAT中，并且两种模型的新功能都可以使用激光雷达测深法作为输入。这项工作介绍了HyCIAT工具箱，并使用模拟数据和实际机载高光谱图像评估了模型性能。结果表明，当将激光雷达数据包含在模型中时，参数检索的准确性会提高。

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  • 作者

    Torres Madronero, Maria Constanza.;

  • 作者单位

    University of Puerto Rico, Mayaguez (Puerto Rico).;

  • 授予单位 University of Puerto Rico, Mayaguez (Puerto Rico).;
  • 学科 Engineering Electronics and Electrical.;Remote Sensing.
  • 学位 M.E.
  • 年度 2009
  • 页码 92 p.
  • 总页数 92
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;遥感技术;
  • 关键词

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