Study on Hyperspectral Conifer Species Classification Based on Improved VGG16 Network

In order to solve the difficult problems of low classification accuracy and long training time in conifer species recognition, this paper presents a coniferous species classification network model based on convolutional neural network in airborne hyperspectral images. VGG16 was selected as the basic network to improve the experiment, which simplified the structure of the network layer, reorganized the arrangement of convolution kernel, and better adapted to the task of hyperspectral classification. The airborne hyperspectral image data band of the teapot experimental forest selected for the experiment was enhanced, the Adam optimizer was used for training optimization, and the learning rate inverse time attenuator and the Early-stooping optimizer were used to accelerate the speed of network fitting and increase the generalization ability of the network. The results showed that, in the case of small inter-class gap and large intra-class gap, compared with the unimproved VGG16 network with the best comparative experiment effect, the accuracy of multi-label classification of hyperspectral images of coniferous species was improved by 8.17%, and the classification accuracy was 94.47%, and the training time was reduced by more than 6 times. It can be concluded that the arrangement of the number of convolution kernels from large to small was helpful to the extraction of hyperspectral information and shorten the training time. The simplification of network layers can ensure that the model can be fitted without excessive training and reduce the training time. Data enhancement was of great help to the improvement of coniferous species identification accuracy.