基于微调CaffeNet的林业图像分类

Forest Image Classification Based on Fine-Tuning CaffeNet

【目的】基于迁移学习提出一种微调卷积神经网络(CNN)的林业图像自动分类方法,以利于林业管理部门对相关事件作出合理的处置方案或指挥调度决策,从而提升森林管护水平,保护森林资源和生态安全。【方法】基于大规模辅助图像数据集ImageNet预训练好的CaffeNet模型,利用林业图像数据对模型进行微调训练。模型前5层参数通过迁移获得,包括卷积层、激活函数和池化层;全连接层和Softmax参数通过训练确定。【结果】微调预训练卷积神经网络CaffeNet模型具有很好的林业图像分类正确率,在根据林业业务需求建立的4类林业图像数据集上,经过一定次数迭代后,平均识别精度达97.5%。进一步特征可视化显示,训练好的深度卷积神经网络不同层获得的特征图可从不同方面获得林业图像分类能力。与传统特征提取方法相比,即使分类种类数增加1种,识别率也可提升10.8%。【结论】利用CaffeNet模型进行林业图像分类可行。相比传统特征提取识别方法,基于卷积神经网络的林业图像分类模型具有很强的特征提取和分类能力,能够在森林管护中发挥重要作用。

【Objective】A fine-tuning convolution neural network method based on transfer learning technology was proposed for automatic classification of forestry images,which was expected to be beneficial for forestry management department to make reasonable disposal plan or scheduling decision in a series of forest incidents,so as to improve the level of forest management and to protect forest resources and ecological safety.【Method】Pre-training with the large-scale image set(ImageNet),CaffeNet was trained with fine-tuning by using the forestry image data.The first 5 layers’parameters of the model were obtained by migration,including convolution layer,activation function and pool layer;and the parameters of full connection layer and Softmax were determined by training.【Result】CaffeNet model of the pre-training fine-tuning convolution neural network presented a good classification accuracy for forestry images.With four classes forestry image data sets established according to forestry business requirements,the average recognition accuracy was stable to 97.5%after a certain number of iterations.Further feature visualization showed that the trained feature maps obtained from different layers of the deep convolution neural network got the forestry image classification ability from different aspects.Compared with the traditional feature extraction method,our method’s recognition rate was also increased by 10.8%even adding one class into the number of forestry categories.【Conclusion】It might be feasible to classify the forestry images by using CaffeNet model.Compared with the traditional feature extraction and recognition methods,the forest image classification model based on convolution neural network could have a stronger feature extraction ability and classification ability.So it would play an important role in the application of forest management in the future.