Abstract: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.