摘要
在高原山地地区,传统遥感分类方法分类精度低,而标准BP神经网络分类方法在实际应用中也难以胜任。探讨对数据源主成分分析特征选择的基础上,用量化共轭梯度法改进标准BP算法,采用GA优化BP网络的隐层神经元数目、初始权重。并以香格里拉县ETM+遥感图像为例,在DEM地形数据辅助下,训练网络使其收敛,仿真输出。结果表明,其分类总精度为84.52%,Kappa系数为0.8317,比最大似然法分类精度提高了9.08个百分点,验证了GA优化的BP网络遥感图像分类的可行性和有效性。
The accuracy of classification was very low for remote sensing imagery of mountain areas in the highlands with traditional method,and it wasn＇t satisfactory with the method of standard Back-Propagation neural networks in practical applications too.The new method was presented,with neural networks and genetic algorithm toolbox of the Matlab for the platform,using conjugate gradient method to improve the standard BP algorithm,using GA to optimize the BP network to identify the number of hidden layer neurons and the initial weights.For example,the ETM ＋ remote sensing image of Shangri-La County was classified in this method.The results showed that the Kappa coefficient was 0.8317,the overall classification accuracy was 84.52%,and it was improved 9.08 percentage points,compare with the maximum likelihood classification method.And it showed that it was feasible and effective to classify the remote sensing imagery by the BP network based on the optimization of genetic algorithm.
出处
《宜春学院学报》
2010年第4期1-4,共4页
Journal of Yichun University
基金
国家自然科学基金项目"三江并流区森林生态系统碳储量遥感定量研究"(40861009)资助
关键词
GA
BP人工神经网络
主成分分析
遥感图像分类
Genetic Algorithm
BP Neural Networks
Principal Component Analysis
Remote Sensing Image Classification
