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A Study of Image Classification of Remote Sensing Based on Back-Propagation Neural Network with Extended Delta Bar Delta

机译：基于扩展Delta Bar Delta的反向传播神经网络的遥感图像分类研究

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摘要

This paper proposes a model to extract feature information quickly and accurately identifying what cannot be achieved through traditional methods of remote sensing image classification. First, process the selected Landsat-8 remote sensing data, including radiometric calibration, geometric correction, optimal band combination, and image cropping. Add the processed remote sensing image to the normalized geographic auxiliary information, digital elevation model (DEM), and normalized difference vegetation index (NDVI), working together to build a neural network that consists of three levels based on the structure of back-propagation neural and extended delta bar delta (BPN-EDBD) algorithm, determining the parameters of the neural network to constitute a good classification model. Then determine classification and standards via field surveys and related geographic information; select training samples BPN-EDBD for algorithm learning and training and, if necessary, revise and improve its parameters using the BPN-EDBD classification algorithm to classify the remote sensing image after pretreatment and DEM data and NDVI as input parameters and output classification results, and run accuracy assessment. Finally, compare with traditional supervised classification algorithms, while adding different auxiliary geographic information to compare classification results to study the advantages and disadvantages of BPN-EDBD classification algorithm.
机译：本文提出了一种快速，准确地提取特征信息的模型，以识别传统遥感图像分类方法无法实现的特征。首先，处理选定的Landsat-8遥感数据，包括辐射校准，几何校正，最佳波段组合和图像裁剪。将处理后的遥感影像添加到归一化的地理辅助信息，数字高程模型（DEM）和归一化差异植被指数（NDVI）中，共同构建一个基于反向传播神经网络结构的由三个层次组成的神经网络以及扩展德尔塔巴德尔塔（BPN-EDBD）算法，确定神经网络的参数，以构成良好的分类模型。然后通过实地调查和相关的地理信息确定分类和标准；选择训练样本BPN-EDBD进行算法学习和训练，并在必要时使用BPN-EDBD分类算法修改和改进其参数，以对经过预处理的遥感图像进行分类，并以DEM数据和NDVI作为输入参数和输出分类结果，以及运行准确性评估。最后，与传统的监督分类算法进行比较，同时添加不同的辅助地理信息来比较分类结果，以研究BPN-EDBD分类算法的优缺点。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第20期|178598.1-178598.10|共10页
  • 作者

    Zhang Shi Liang; Chang Ting Cheng;

  • 作者单位

    Ningde Normal Univ, Dept Comp Sci, Ningde 352100, Fujian, Peoples R China;

    Ningde Normal Univ, Dept Comp Sci, Ningde 352100, Fujian, Peoples R China;

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