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首页> 外文学位 >Modularizing backpropagation neural networks for multisource spatial data modeling and classification.
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Modularizing backpropagation neural networks for multisource spatial data modeling and classification.

机译：模块化的反向传播神经网络，用于多源空间数据建模和分类。

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摘要

Applications of artificial neural networks (ANN) in remote sensing and multisource spatial data classification have been frequently reported in the past several years. In the previous research, backpropagation ANN (BPANN) has commonly been applied. This popularity primarily revolves around the ability of backpropagation paradigm to learn complicated multidimensional mapping. Other ANN paradigms, however, are seldom applied and reported. Technically, the demand of development of efficient ANN architectures for handling multisource spatial data still remains. In multisource spatial data land cover classification, while more information can be supplied by those data, noise, redundancy and confusion may also be introduced. If artificial neural network paradigms with modular functions or competition mechanisms can be developed, the information process for each data source will be decomposed, and the contribution of each data set will be separately evaluated. Therefore, the advantages from each data source will be discovered and efficiently employed to perform the classification. To reach this goal, other architectures of neural network paradigms, such as modular artificial neural network (MANN) and learning vector quantization (LVQ) network, are alternatives requiring further investigation.; In this research, three network paradigms of BPANN, MANN, and LVQ were developed and evaluated in multisource spatial data land cover classifications. Traditional maximum likelihood classification (MLC) was also compared in classification performance. Multisource spatial data in different combinations were classified by the three ANN paradigms, respectively. Comparable land cover classification results were achieved by BPANN and MLC. Both MANN and LVQ networks achieved better classification results than MLC. It is concluded that all three artificial neural network paradigms can be applied in high dimensional multispectral, multitemporal, multisource spatial data classification. With a modular design and competition mechanism, the MANN and LVQ networks performed well and are the reliable alternatives in multisource spatial data land cover classification.
机译：过去几年来，人工神经网络（ANN）在遥感和多源空间数据分类中的应用得到了广泛报道。在先前的研究中，反向传播ANN（BPANN）已被普遍应用。这种流行主要围绕反向传播范例学习复杂的多维映射的能力。但是，很少使用和报告其他ANN范例。从技术上讲，仍然需要开发高效的ANN架构来处理多源空间数据。在多源空间数据土地覆被分类中，尽管可以通过这些数据提供更多信息，但也可能引入噪声，冗余和混乱。如果可以开发具有模块化功能或竞争机制的人工神经网络范式，则将分解每个数据源的信息过程，并将分别评估每个数据集的贡献。因此，将发现并有效地利用每个数据源的优势进行分类。为了达到这个目标，其他的神经网络范式架构，例如模块化人工神经网络（MANN）和学习矢量量化（LVQ）网络，是需要进一步研究的替代方案。在这项研究中，开发了BPANN，MANN和LVQ的三个网络范例，并在多源空间数据土地覆盖分类中对其进行了评估。还比较了传统最大似然分类（MLC）的分类性能。通过三种ANN范例分别对不同组合中的多源空间数据进行了分类。 BPANN和MLC取得了可比的土地覆盖分类结果。 MANN和LVQ网络均比MLC获得更好的分类结果。结论是，所有三种人工神经网络范例都可以应用于高维多光谱，多时间，多源空间数据分类。借助模块化设计和竞争机制，MANN和LVQ网络表现良好，并且是多源空间数据土地覆被分类中的可靠替代方案。

著录项

  • 作者

    Wang, Yeqiao.;

  • 作者单位

    University of Connecticut.;

  • 授予单位 University of Connecticut.;
  • 学科 Physical Geography.; Agriculture Forestry and Wildlife.; Remote Sensing.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1995
  • 页码 183 p.
  • 总页数 183
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自然地理学;森林生物学;遥感技术;人工智能理论;
  • 关键词

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