College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China.
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China.
Sensors (Basel). 2022 Apr 20;22(9):3157. doi: 10.3390/s22093157.
The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy.
树种个体分类（ITS）有利于森林管理和保护。基于机载激光雷达和航空照片的 ITS 分类的先前研究已经达到了最高的分类精度。然而，由于数据采集复杂且成本高，很难将 ITS 分类应用于大面积森林的分类。高分辨率卫星遥感数据在 ITS 分类中具有丰富的来源和重要的应用潜力。基于 Worldview-3 和谷歌地球图像，卷积神经网络（CNN）模型被用于通过充分利用不同季节图像中包含的特征信息来提高 ITS 的分类精度。在这三个 CNN 模型中，DenseNet 的表现优于 ResNet 和 GoogLeNet。仅使用 WorldView-3 图像，DenseNet 对 7 个树种的 OA 为 75.1%，而使用 WorldView-3 和秋季谷歌地球图像的组合，OA 为 78.1%。结果表明，具有适当时间细节的谷歌地球图像可以作为辅助数据用于提高分类精度。
