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首页> 外文学位 >An artificial immune network approach to land use / land cover classification using multi-sensor remote sensing data.
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An artificial immune network approach to land use / land cover classification using multi-sensor remote sensing data.

机译：使用多传感器遥感数据对土地利用/土地覆被进行分类的一种人工免疫网络方法。

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摘要

An optimized immune network-based classification (OPTINC) method was developed and adapted for land use / land cover classification. Based on the widely-used artificial immune network (aiNet) model, three major improvements were made: (1) the preservation of the best antibodies for each class from being suppressed; (2) the usage of self-adaptive mutation rates in response to changes in model performance between learning generations; and (3) the integration of genetic algorithm-optimized linear combinations of Euclidean distance and spectral angle mapping distance as affinity measurements. OPTINC was evaluated for two study sites with multi-sensor data. Decision trees, neural networks and aiNet were also tested and compared in terms of classification accuracy, local homogeneity of the classified image, and model sensitivity to sample size. OPTINC outperformed the other models with higher accuracy and much less salt-and-pepper noise in the classification images. OPTINC was relatively less sensitive to training sample size than decision trees and neural networks were.;Key Words: Artificial immune networks; Artificial neural networks; Decision trees; Land use / land cover classification
机译：开发了一种优化的基于免疫网络的分类（OPTINC）方法，并适用于土地利用/土地覆盖分类。在广泛使用的人工免疫网络（aiNet）模型的基础上，进行了三项重大改进：（1）防止每种类别的最佳抗体受到抑制； （2）使用自适应突变率来响应学习代之间模型性能的变化； （3）整合遗传算法优化的欧几里得距离和光谱角度映射距离的线性组合作为亲和力度量。对OPTINC的两个研究地点进行了多传感器数据评估。还对决策树，神经网络和aiNet进行了测试，并在分类准确性，分类图像的局部均匀性以及模型对样本大小的敏感性方面进行了比较。在分类图像中，OPTINC的精度更高，盐和胡椒的噪声要少得多，优于其他模型。与决策树和神经网络相比，OPTINC对训练样本的敏感性相对较低。人工神经网络;决策树；土地利用/土地覆被分类

著录项

  • 作者

    Gong, Binglei.;

  • 作者单位

    State University of New York College of Environmental Science and Forestry.;

  • 授予单位 State University of New York College of Environmental Science and Forestry.;
  • 学科 Geodesy.;Remote Sensing.
  • 学位 M.S.
  • 年度 2010
  • 页码 94 p.
  • 总页数 94
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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