基于预训练模型与无人机可见光影像的树种识别

提高图像质量与利用新的图像分类方法是提高遥感图像树种识别精度两个突破口. 本文基于VGG16的预训练模型与无人机可见光影像进行杉木、马尾松2个树种识别研究. 利用大疆精灵4RTK无人机, 搭载FC6310R相机, 采集南平市和三明市的杉木和马尾松人工纯林彩色图像. 通过图像预处理、标注、裁剪和增强等环节构建UAVTree2k和UAVTree20k两个数据集. 基于UAVTree2k数据集和VGG16模型在ImageNet数据集的预训练模型, 重新训练3个全连接层和Sigmoid层, 研究探讨不同迭代次数、不同批次大小、不同训练集和测试集划分比例对识别精度的影像. 研究结果表明, 当迭代次数为40、批次大小为16、训练集和测试集为6:4时, 模型识别效果最好, 测试精度达到98.63%; 小样本下, 基于VGG16的预训练模型具有良好的特征学习能力.

Tree Species Identification by Visible Image from Pre-trained Model and UAV

Enhancing image quality and adopting new image classification methods are two breakthrough points to improve the accuracy of tree species identification by remote sensing images. The research focuses on the identification of Chinese fir and Masson pine by the pre-trained model of VGG16 and unmanned aerial vehicle (UAV) visible images. The DJI Phantom 4RTK UAV with an FC6310R camera is used to collect color images of artificial pure forests of Chinese fir and Masson pine in Nanping and Sanming cities. Then, two datasets UAVTree2k and UAVTree20k are constructed through image preprocessing, annotation, cropping, and enhancement. Furthermore, three full connection layers and Sigmoid layer are trained by the UAVTree2K dataset and the pre-trained model of VGG16 on the ImageNet dataset to investigate the effects of the number of iterations, batch size, partition ratios of the training set and the test set on identification accuracy. The results show that when the number of iterations is 40, the batch size is 16, and the ratio between the training set and the test set is 6:4, the identification effect of the model is best, and the test accuracy reaches 98.63%. Meanwhile, the VGG16-based pre-trained model has a good feature learning ability for a small sample size.