| 基于BP神经网络的遥感影像分类研究 |
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| 引用本文： | 卢柳叶,张青峰,李光录.基于BP神经网络的遥感影像分类研究[J].测绘科学,2012,37(6):140-143. |
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| 作者姓名： | 卢柳叶 张青峰 李光录 |
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| 作者单位： | 西北农林科技大学资源环境学院,陕西杨凌,712100 |
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| 基金项目： | 中央高校基本科研业务费，陕西省自然科学基金 |
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| 摘 要： | BP神经网络具有收敛速度快和自学习、自适应功能强的特点,能最大限度地利用样本集的先验知识,自动提取合理的模型。本文采用Landsat TM遥感图像作为数据源,以山西省定襄县为研究区,通过主成分分析方法来压缩输入数据,并结合NDVI和纹理特征来建立BP神经网络的土地利用分类模型,将分类结果与基于光谱单元信息的神经网络分类和基于纹理特征的神经网络分类结果进行定性和定量比较分析。结果表明:该方法总精度达到了80.50%,分别比基于光谱单元信息的神经网络分类和基于纹理特征的神经网络分类提高了18.89%和6.23%,能够有效地解决地物光谱混淆、分类精度不高等问题。
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| 关 键 词： | NDVI 纹理特征 神经网络 土地利用 PCA |
Image classification of remote sensing based on BP neural networks |
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| LU Liu-ye
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ZHANG Qing-feng
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LI Guang-lu.Image classification of remote sensing based on BP neural networks[J].Science of Surveying and Mapping,2012,37(6):140-143. |
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| Authors: | LU Liu-ye ZHANG Qing-feng LI Guang-lu |
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| Institution: | (College of Resources and Environment,Northwest A&F University,Shaanxi Yangling 712100,China) |
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| Abstract: | Remote sensing image classification technology is an important segment of image analysis and interpretation.B PANN has the characteristic of faster convergence speed and stronger capabilities of self-study and self-adaptation,it's able to utilize the priori knowledge of data sets furthest to automatically determine reasonable models,and model prediction results can be realistic to reflect the real surface features.This paper adopted Landsat TM images for data sources,Dingxiang country of Shanxi province was taken as the research area,principal component analysis was used to compress the data,a comprehensive model of B PANN integrated NDVI with texture features was adopted to classify regional land use information,finally,the result was compared with the classification based on the method of spectrum cell information and Texture Features-based B PANN separately.The result showed that the accuracy of this method is 80.50%,which increases by18.89% compared with spectrum cell information based ANN classification,and 6.23% compared with texture features based ANN classification separately.It could effectively solve the problems of spectral confusion and low classification accuracy. |
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| Keywords: | NDVI texture feature ANN land use PCA |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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