基于深度信念网络的高光谱影像森林类型识别

森林类型分类对森林生态系统管理起重要作用,高光谱影像由于波段多,传统方法先对其进行特征选择或特征提取进行降维处理,再进行图像分类,一定程度影响森林类型识别精度.深度信念网络是一种半监督学习方法,可将高光谱所有波段作为深度信念网络的输入,从而避免降维处理.论文利用深度信念网络对泉州市德化县西部8个乡镇进行森林类型识别研究.基于HJ/1A高光谱图像与二类调查数据,利用Python语言实现高光谱影像森林类型分类,讨论了网络深度和隐藏层单元数对总体精度与Kappa系数的影响.实验结果表明:层数为3,每层节点数为256的网络结构对森林类型识别效果最好,总体精度达85.8%,系数为0.785,好于支持向量机的分类结果.

Forest Type Classification by Hyperspectral Image Using Deep Belief Network

The classification of forest types plays an important role in the management of forest ecosystems. Because of the large number of bands in hyperspectral imagery, the traditional methods of dimensionality reduction include features selection or feature extraction, affect the accuracy of forest type identification to a certain extent. The Deep Belief Network (DBN) is a semi-supervised learning method that uses all bands of hyperspectral image as input to avoid dimensionality reduction. Forest type identification of 8 townships in the west of Dehua County in Quanzhou was carried out. At the beginning, the classification of forest types in hyperspectral imagery was realized by Python language, according to HJ/1A hyperspectral image and forest management data. In addition, the influence of network depth and number of hidden layer units on overall accuracy and Kappa coefficient was discussed. The experimental results show that the network with 3 layers and 256 nodes is the optimal structure for forest type identification. The overall accuracy is 85.8% and the coefficient is 0.785, which is better than the classification result of support vector machine.