摘要
【目的】林业业务图像的识别分类有利于林业管理部门对相关事件作出合理的处置方案及指挥调度决策,从而充分发挥护林员的作用,提升森林管护的水平,达到保护森林资源和生态安全的目的。【方法】提出了一种针对林业业务图像基于迁移学习的卷积神经网络(convolutional neural networks)自动分类模型。在经过大规模辅助图像数据集ImageNet预训练的4种卷积神经网络模型的基础上,使用林业业务图像数据对训练好的模型进行迁移学习,采用新的全连接层取代原始的全连接层,其他层参数保持不变。【结果】在建立的4个类别林业业务图像数据集上,4个预训练卷积神经网络结构的迁移学习模型都具有较高的分类正确率。其中,基于Inception-v3的迁移学习模型识别精度最高,达到96.4%。【结论】利用基于Inception-v3的迁移学习模型进行林业业务图像分类是可行的。相比传统的特征提取识别方法以及其他预训练模型,Inception-v3模型具有很强的分类能力,可以在森林管护中发挥更广泛的应用。
【Objective】The recognition and classification of forestry business images are helpful for forestry administrative departments to make reasonable disposal plans,instruct others,and communicate decisions for relevant events.Therefore,full information can be provided to the forest protection personnel,which will improve the level of forest management and protection. Then,the protection of forest resources and ecological security can be achieved.【Method】We proposed an automatic classification model of convolutional neural networks based on the transfer learning for forestry business images. Four convolutional neural network models were pre-trained using a large-scale auxiliary image dataset,called ImageNet. Then,the forestry business image data,with a relatively small number of images,were used to transfer the trained model. The classification accuracy was compared and analyzed to select the model with the best classification effect. In this study,four convolutional neural network models were selected.The forestry business image dataset used for the model training and testing contained four classes,including animal death(AD),forest harvesting(FH),forest fire(FF),and forest pests(FP). The dataset included a total of 280 images,with 70 images in each class. The forestry business image dataset was divided into a training and test set,which accounted for 90% and 10%,respectively. The 10-fold cross-validation method was used to obtain the final test accuracy rate. A new fully connected layer was used to replace the original fully connected layer,while the other layers remained unchanged. The transfer learning method was dividedinto two steps. The Adam optimization algorithm was used to train the models. In addition,to compare the impacts of different data enhancement methods on the classification effect,three experimental scenarios were used in this study.【Result】The transfer learning models that we used with the four pre-trained convolutional neural network structures all had a higher classification accuracy
作者
林朝剑
张广群
杨洁
徐鹏
李英杰
汪杭军
LIN Chaojian;ZHANG Guangqun;YANG Jie;XU Peng;LI Yingjie;WANG Hangjun(School of Information Engineering,Zhejiang A&F University,Hangzhou 311300,China;Jiyang College,Zhejiang A&F University,Zhuji 311800,China)
出处
《南京林业大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第4期215-221,共7页
Journal of Nanjing Forestry University：Natural Sciences Edition
基金
浙江省自然科学基金项目(LY16C160007)
浙江省基础公益研究计划项目(LGN19C140006)
绍兴市科技计划项目(2018C20013)
人才启动项目(JY2018RC04)。
关键词
林业业务图像
迁移学习
森林管护
卷积神经网络
图像识别
forestry business image
transfer learning
forest manage and protect
convolutional neural network
image recognition
