1. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China 2. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China 3. Forestry Post-Doctoral Station of Fujian Agriculture and Forestry University, Fuzhou 350002, China 4. Key Laboratory for Ecology and Resource Statistics of Fujian Province, Fuzhou 350002, China
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.
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2019, Vol. 31 
