【摘要】 使用低空遥感图像进行图像识别为森林调查和监测提供了新的技术契机。基于无人机低空航拍光学图像,以福建省安溪县崩岗区为研究区,建立FC-Dense Net模型进行树种识别。首先,利用Dense模块提取树种图像特征并增强深层网络信息,透过下采样模块降低图像维度,凸显图像的纹理特征和光谱特征;然后,使用上采样模块还原预测图至原始图像大小,并融合浅层Dense模块信息的丰富特征;最后,采用Softmax分类器实现像素分类,完成树种识别。结果显示,基于低空航拍光学图像,FC-Dense Net模型能够准确区分植被与非植被,定位其空间分布特征,其中,FC-Dense Net-103模型的二分类识别精度为92. 1%,表明FC-Dense Net模型加深网络深度后具有较好的识别效果;将植被与非植被细分为13类,FC-Dense Net-103模型的平均识别正确率达到75. 67%。研究结果表明,基于低空航拍光学图像建立的FC-Dense Net模型具有较高的树种分类精度。由于低空航拍光学图像的成本较低,数据获取费用小,时间周期短,可便于森林资源调查和森林树种检测,为深度学习在树种识别领域的应用提供了新思路。更多还原

【Abstract】 Image recognition based on low-altitude remote sensing imageries provides a new technological opportunity for forest survey and monitoring. In this study,the authors took the permanent gully in Benggang District,Anxi County,Fujian Province,as an instance and constructed the FC-DenseNet to identify tree species based on the low-altitude aerial optical image of UAV. First,the dense module in the FC-DenseNet model can extract the features of spectral images and enhance the information of the deep network,and the transition down block has an impact on reducing the image dimensions and highlighting the texture and spectral features; then,the transition up block can resize the scale of the predicted image to that of the original image,combined with information fusion of the shallow Dense module; finally,the Softmax classifier is used to achieve pixel-level classification so as to complete the tree species recognition. The results are as follows: ①The FC-DenseNet model based on the low-altitude aerial images not only could identify the difference between vegetation and non-vegetation but also could detect the their spatial distribution. The accuracy of the FC-DenseNet-103 model for vegetation and non-vegetation pixels is 92. 1%,and the 103 layers’ network layer is the best network layer. ②Tree species are subdivided into 13 categories,and the accuracy of FC-DenseNet-103 model for dominant species reaches 79%. Some conclusions have been reached: The FC-DenseNet model based on low-altitude aerial optical images has a high tree classification accuracy. With the low cost of low-altitude aerial optical imagery,low data acquisition costs and short time cycles,forest resource surveys and forest species detection can be facilitated. The results obtained by the authors provide a new method in the field of tree recognition using deep learning.更多还原