基于深度学习的遥感图像茶园区域识别应用研究

得益于遥感技术的发展和深度学习在图像处理方面的进展,采用深度学习识别遥感图像的方法被广泛应用。与传统的统计农作物种植面积方法相比较,通过深度学习的方法来识别茶园种植区域,可以减少人工依赖,节约人力资源,实时获取数据,具有更高的时效性。数据来源于Bigmap,以贵州省卫星遥感图像为数据基础,提出了使用深度学习来识别茶园区域的应用方法。实验目标为从整张遥感图像中提取出茶园种植区域。首先对遥感图像进行数据预处理,然后采用人工目视解译的方法标注出茶园区域并制成数据集,将数据集导入神经网络进行训练获得网络模型,最后将验证图像放入到训练好的神经网络当中,获得验证结果;检测精确率为95.83%,检测召回率为85.00%。

Tea Plantation Classification Techonology Using Remote Sensing Image Based Deep Learning

Benefit from the development of remote sensing technology and the progress of deep learning in image processing,the method of recognizing remote sensing images using deep learning is widely used.Compared with the traditional method of statistical crop planting area,using deep learning to identify tea plantation areas can reduce manual dependence,save human resources,and obtain data in real time,which has higher timeliness.The data in this article comes from Bigmap.Based on the satellite remote sensing images of Guizhou Province,an application method using deep learning to identify tea plantation areas was proposed.The experimental goal is to extract the tea plantation area from the entire remote sensing image.Firstly preprocess the data of the remote sensing image,then use artificial visual interpretation to label the tea plantation area and make a data set.The data set is imported into the neural network for training to obtain a network model.Finally,the verification image is put into the trained In the neural network,obtain the verification results.The detection accuracy rate was 91.71%,the detection recall rate was 80.41%,and the detection specificity rate was 94.72%.