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Large-scale feature selection from imaging spectrometer data using a genetic algorithm for invasive species mapping and monitoring.

机译：使用遗传算法从成像光谱仪数据中进行大规模特征选择，用于入侵物种的制图和监测。

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摘要

Invasive species pose the single greatest threat of natural disaster in this century. With rare exceptions, the estimated annual cost of invasive species to the U.S. exceeds all other natural disasters combined. Invasive plant species can rapidly displace native vegetation and upset the balance in an ecosystem. Accurate and rapid identification and monitoring of effected areas are critical to maintaining control in environments where invasive species have or could develop.The goal of this research is to develop a method for selecting the near-optimal spectral band subset from hyperspectral imagery (HSI) data to improve classification accuracy and feature transparency. The research presented in this dissertation is focused on the implementation and testing of a genetic algorithm (GA) to reduce the number of spectral bands that are needed to produce an accurate and rapid classification map using HSI data. This algorithm was applied to the invasive species mapping and monitoring problem and can be used for similar "needle in a haystack" problems.This research has also provided a sensitivity analysis for a number of the parameters used by a GA including: the chromosome population size the number of generations required achieve an optimal feature set the crossover and mutation rates and the size of the feature subset. This research should provide a base for additional research into the application GA's for feature selection and extraction in high dimensional remote sensing data. Statistics of the time it takes to run the algorithm have also been captured in order to give users a sense of how this algorithm compares to the runtime for other feature extraction methods such as the Minimum Noise Fraction (MNF) algorithm.The contribution of this research is a demonstrated method to provide a high accuracy feature selection technique to identify invasive species that provides direct feature traceability to class spectra, through the development of a genetic algorithm. The method can be easily adapted to high dimensional remote sensing problems beyond the one addressed in this research such as extending it to the selection of optimal features from multiple remote sensing data types (spatial and texture features in panchromatic or SAR data in addition to spectral).
机译：入侵物种构成了本世纪自然灾害的最大威胁。除极少数例外，美国每年入侵物种的估计成本超过所有其他自然灾害的总和。外来入侵植物可以迅速取代本地植被，破坏生态系统中的平衡。准确，快速地识别和监控受影响的区域对于在存在或可能存在入侵物种的环境中保持控制至关重要。本研究的目的是开发一种从高光谱影像（HSI）数据中选择近最佳光谱子集的方法提高分类准确性和功能透明度。本文的研究集中在遗传算法的实现和测试上，以减少使用HSI数据生成准确，快速的分类图所需的光谱带数量。该算法已应用于入侵物种的制图和监测问题，可用于类似的“大海捞针”问题。这项研究还为遗传算法使用的许多参数提供了敏感性分析，包括：染色体种群大小获得最佳特征所需的世代数设置了交叉和变异率以及特征子集的大小。该研究应为进一步研究GA在高维遥感数据中进行特征选择和提取的应用奠定基础。为了使用户了解该算法与其他特征提取方法（如最小噪声分数（MNF）算法）的运行时间相比，该算法运行时间的统计数据也已被捕获。是一种经过证明的方法，可通过开发遗传算法来提供一种高精度特征选择技术，以识别可提供直接特征溯源到类光谱的入侵物种。该方法可以轻松地解决本研究中未解决的高维遥感问题，例如将其扩展到从多种遥感数据类型中选择最佳特征（除光谱之外，全色或SAR数据中的空间和纹理特征） 。

著录项

  • 作者

    Huth, John F.;

  • 作者单位

    George Mason University.;

  • 授予单位 George Mason University.;
  • 学科 Environmental Sciences.Remote Sensing.Computer Science.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 158 p.
  • 总页数 158
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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