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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 225-233     DOI: 10.6046/gtzyyg.2019.03.28
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Tree species recognition of UAV aerial images based on FC-DenseNet
Zhiwei LIN1,2,3, Weihao TU1, Jiahang HUANG1, Qilu DING1, Zhengwen ZHOU1, Jinfu LIU1,4
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
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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.

Keywords FC-DenseNet      optical image      tree species recognition      unmanned aerial vehicle      deep neural network     
:  X835TP79  
Issue Date: 30 August 2019
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Zhiwei LIN
Weihao TU
Jiahang HUANG
Qilu DING
Zhengwen ZHOU
Jinfu LIU
Cite this article:   
Zhiwei LIN,Weihao TU,Jiahang HUANG, et al. Tree species recognition of UAV aerial images based on FC-DenseNet[J]. Remote Sensing for Land & Resources, 2019, 31(3): 225-233.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.28     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/225
高度 A4白纸像
素点数
A4纸实际
面积/cm2
1像素点所
占面/cm2
20 m 1 273 623.7 0.490
Tab.1  Table of Aerial video resolution
Fig.1  Test map of 20 m aerial image resolution
Fig.2  Image enhancement schematic
Fig.3  Proportion of pixels in each category of training data
Fig.4  Frames of FC-DenseNet
Fig.5  Sketch map of the Dense module
Fig.6  Sketch map of receptive field
FC-DenseNet-56 FC-DenseNet-103
输入层 输入层
3×3卷积层 3×3卷积层
DB (4层) +TD DB (4层) +TD
DB (4层) +TD DB (5层) +TD
DB (4层) +TD DB (7层) +TD
DB (4层) +TD DB (10层) +TD
DB (4层) +TD DB (12层) +TD
DB (4层) DB (15层)
TU + DB (4层) TU + DB (12层)
TU + DB (4层) TU + DB (10层)
TU + DB (4层) TU + DB (7层)
TU + DB (4层) TU + DB (5层)
TU + DB (4层) TU + DB (4层)
1×1卷积层 1×1卷积层
Softmax Softmax
Tab.2  Table of FC-DenseNet network structure
Fig.7  Graph of training loss
Fig.8  Graph of different models classification
模型 PA MPA mIoU fIoU
FC-DenseNet-56 91.7 62.6 41.6 88.1
FC-DenseNet-103 92.1 63.7 44.9 88.7
Tab.3  Classification results for different models (%)
Fig.9  Classification of FC-DenseNet-103 dominant species
Fig.10  Schematic diagram of classification effect of dominant tree species
Fig.11  Classification accuracies of the object-based algorithm for dominant tree species
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