基于BP神经网络的遥感影像分类
Remote Sensing Image Classification Based on BP Neural Network
传统统计模式识别方法进行遥感影像分类时要求数据服从正态分布，并且存在分类精度低的缺点。通过分析BP网的分类原理与学习算法，选择最能反映研究区土地利用信息的光谱数据，进行BP网的训练分类。将分类结果与采用最大似然法所得的结果综合比较，结果表明，该方法优于最大似然法。
he traditional statistical classifier is suitable in making RS image classification in normal distribution with its low precition. After analyzing the principle and learning algorithms of BPNN, land use classification of BPNN is acquired by selecting optimized spectral data.The classification results are compared with the results obtained by Maximum Likelihood classifier. Experimental results show that BPNN is superior to the latter in the accuracy of classification.
俞冰
遥感技术
遥感BP神经网络影像分类最大似然法
remote sensingBP neural networkimage classificationmaximum likelihood classifier
俞冰.基于BP神经网络的遥感影像分类[EB/OL].(2007-02-08)[2025-08-02].http://www.paper.edu.cn/releasepaper/content/200702-111.点此复制
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