Image recognition of tree species based on multi feature fusion and CNN model

Liu Jiazheng; Wang Xuefeng; Wang Tian

doi:10.13332/j.1000-1522.20180366

Journal of Beijing Forestry University > 2019 > 41(11): 76-86. > DOI: 10.13332/j.1000-1522.20180366

Liu Jiazheng, Wang Xuefeng, Wang Tian. Image recognition of tree species based on multi feature fusion and CNN model[J]. Journal of Beijing Forestry University, 2019, 41(11): 76-86. DOI: 10.13332/j.1000-1522.20180366

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Image recognition of tree species based on multi feature fusion and CNN model

Research Institute of Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

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Received Date: November 11, 2018
Revised Date: March 07, 2019
Available Online: September 30, 2019
Published Date: October 31, 2019

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Abstract

Abstract

ObjectiveThere are intra-class differences and inter-class similarities in tree species image recognition, which makes it difficult for traditional methods based on single artificial features to achieve ideal recognition results. In order to solve these problems, a tree image depth learning recognition method based on convolution neural network was proposed, which combines deep features of the image with artificial features.
MethodSix kinds of common tree species, including Pinus sylvestris var. mongolica, Populus davidiana, Betula platyphylla, Larix gmelinii, Cedrus deodara and Pinus alba, were studied. Firstly, the original tree species image set was expanded by clipping, horizontal flipping, rotation and other operations, and was divided into training set and test set to establish the image database of this tree species recognition experiment; secondly, the model was designed as three parallel channels. The network selected RGB image, HSV image and LBP-HOG image, respectively, and recognized the above tree image from the point of view of pixel value, color, texture and shape. On the one hand, a CNN depth learning model suitable for this experiment was constructed. The RGB image and the corresponding HSV image in the training set were used as the input of the first and second CNN models to extract the deep features of tree image. On the other hand, the training set was de-noised by Gaussian filtering, and LBP-HOG features were extracted artificially to represent texture and shape features as the input of the third CNN model. Finally, the features obtained by each of the three models were summarized in the last layer of the fully connected layer as the final classification basis of the soft Max classifier. Finally, in order to verify the feasibility of the proposed method, the SVM classifier, BP neural network, the existing depth learning LeNet-5 model and VGG-16 model were trained by the above features and training set, and the test set was identified and verified to compare the final recognition effect.
ResultThe training accuracy of the multi-feature fusion CNN model was 96.13%, and the average recognition accuracy was 91.70%. In the CNN tree species recognition model based on one-way training, the recognition rate of RGB image as training input value was the highest, which was 75.21%, followed by HSV feature recognition rate, and LBP-HOG feature was the worst; in the case of multi-feature fusion, the combination recognition rate of RGB + HSV + LBP + HOG was the highest, which reached 93.50%; in the case of RGB + H channel + LBP + HOG, the recognition rate of RGB + HSV + LBP + HOG was the highest. The recognition rate was 89.50%. Under the same condition of feature or feature combination, the recognition rate of SVM, BP neural network, LeNet-5 model and VGG-16 model was lower than that of the model in this paper.
ConclusionBased on RGB + H channel + LBP feature fusion, the three-way parallel CNN model is used to get the highest recognition rate for the six types of tree images in this paper, which overcomes the problem of low recognition rate in the case of a single feature, and the recognition effect is also very ideal. It realizes automatic recognition of specific categories from a large number of different tree images.
- tree species,
- image recognition,
- feature fusion,
- deep learning,
- convolution neural network

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References

[1]	王昌腾. 基于应用型人才培养提高学生树木识别教改探索[J]. 现代园艺, 2018(13):165−166. Wang C T. Exploration on the teaching reform of improving students'tree recognition based on the cultivation of applied talents[J]. Modern Horticulture, 2018(13): 165−166.
[2]	Richter R, Reu B, Wirth C, et al. The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 52: 464−474. doi: 10.1016/j.jag.2016.07.018
[3]	Pham L T H, Brabyn L, Ashraf S. Combining QuickBird, LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach[J]. International Journal of Applied Earth Observation and Geoinformation, 2016, 50: 187−197. doi: 10.1016/j.jag.2016.03.015
[4]	陈明健, 陈志泊, 杨猛, 等. 叶片传统特征和距离矩阵与角点矩阵相结合的树种识别算法[J]. 北京林业大学学报, 2017, 39(2):108−116. Chen M J, Chen Z B, Yang M, et al. A tree species identification algorithm combining traditional leaf characteristics and distance matrix with corner matrix[J]. Journal of Beijing Forestry University, 2017, 39(2): 108−116.
[5]	李可心, 戚大伟, 牟洪波, 等. 基于灰度共生矩阵与SOM神经网络的树皮纹理特征识别[J]. 森林工程, 2017, 33(3):24−27. doi: 10.3969/j.issn.1006-8023.2017.03.006 Li K X, Qi D W, Mou H B, et al. Bark texture recognition based on gray level co-occurrence matrix and SOM neural network[J]. Forest Engineering, 2017, 33(3): 24−27. doi: 10.3969/j.issn.1006-8023.2017.03.006
[6]	杨洋. 基于小波变换及SVM理论的树木种类识别研究[D]. 哈尔滨: 东北林业大学, 2017. Yang Y. Research on tree species recognition based on wavelet transform and SVM theory[D]. Harbin: Northeast Forestry University, 2017.
[7]	于海鹏, 刘一星, 刘镇波. 基于图像纹理特征的木材树种识别[J]. 林业科学, 2007,43(4):77−81,146−147. doi: 10.3321/j.issn:1001-7488.2007.04.013 Yu H P, Liu Y X, Liu Z B. Wood species identification based on image texture features[J]. Forestry Science, 2007,43(4): 77−81,146−147. doi: 10.3321/j.issn:1001-7488.2007.04.013
[8]	孙伶君, 汪杭军, 祁亨年. 基于分块LBP的树种识别研究[J]. 北京林业大学学报, 2011, 33(4):107−112. Sun L J, Wang H J, Qi H N. Study on tree species identification based on block LBP[J]. Journal of Beijing Forestry University, 2011, 33(4): 107−112.
[9]	Bertrand S, Ameur R B, Cerutti G, et al. Bark and leaf fusion systems to improve automatic tree species recognition[J]. Ecological Informatics, 2018, 46: 57−73. doi: 10.1016/j.ecoinf.2018.05.007
[10]	Zhao Z Q, Ma L H, Cheung Y, et al. ApLeaf: an efficient android-based plant leaf identification system[J]. Neurocomputing, 2015, 151: 1112−1119. doi: 10.1016/j.neucom.2014.02.077
[11]	赵鹏超, 戚大伟. 基于卷积神经网络和树叶纹理的树种识别研究[J]. 森林工程, 2018, 34(1):56−59. doi: 10.3969/j.issn.1006-8023.2018.01.013 Zhao P C, Qi D W. Study on tree species identification based on convolution neural network and leaf texture[J]. Forest Engineering, 2018, 34(1): 56−59. doi: 10.3969/j.issn.1006-8023.2018.01.013
[12]	Li Q, You X, Li K, et al. Deep hierarchical feature extraction algorithm[J]. Pattern Recognition and Artificial Intelligence, 2017, 30(2): 127−136.
[13]	Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278−2324. doi: 10.1109/5.726791
[14]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of Advances in Neural Information Processing Systems. Cambridge: Massachusetts Institute of Technology Press, 2012: 1106−1114.
[15]	Deng J, Dong W, Socher R, et al. ImageNet: a large-scale hierarchical image database[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Washington D C: IEEE Computer Society, 2009: 248−255.
[16]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[C/OL]. arXiv, 2014[2018−05−06]. https://arxiv.org/pdf/1409.1556.pdf.
[17]	Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Washington D C: IEEE Computer Society Press, 2015: 1−9.
[18]	He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, Vegas: IEEE, 2016: 770−778.
[19]	李彦冬, 郝宗波, 雷航. 卷积神经网络研究综述[J]. 计算机应用, 2016, 36(9):2508−2515, 2565. doi: 10.11772/j.issn.1001-9081.2016.09.2508 Li Y D, Hao Z B, Lei H. A review of convolutional neural networks[J]. Computer applications, 2016, 36(9): 2508−2515, 2565. doi: 10.11772/j.issn.1001-9081.2016.09.2508
[20]	Zeiler M D, Fergus R. Stochastic pooling for regularization of deep convolutional neural networks[C/OL]. arXiv, 2013 [2018−04−16]. https://arxiv.org/pdf/1301.3557.pdf.
[21]	Nair V, Hinton G E, Farabet C. Rectified linear units improve restricted Boltzmannmachines[C]//Processing of the 27th International Conference on Machine Learning. Haifa: International Machine Learning Society (IMLS), 2010: 807−714.
[22]	周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6):1229−1251. doi: 10.11897/SP.J.1016.2017.01229 Zhou F Y, Jin L P, Dong J. Summary of convolution neural network research[J]. Acta Computer Science, 2017, 40(6): 1229−1251. doi: 10.11897/SP.J.1016.2017.01229
[23]	刘涛, 周先春, 严锡君. 多通道多模式融合LBP特征的纹理相似度计算[J]. 计算机应用研究, 2018, 35(12):3803−3806. doi: 10.3969/j.issn.1001-3695.2018.12.063 Liu T, Zhou X C, Yan X J. Computation of texture similarity based on multi-channel and multi-mode LBP features[J]. Computer Applications, 2018, 35(12): 3803−3806. doi: 10.3969/j.issn.1001-3695.2018.12.063
[24]	尚俊. 基于HOG特征的目标识别算法研究[D]. 武汉: 华中科技大学, 2012. Shang J. Target recognition algorithm based on HOG features[D]. Wuhan: Huazhong University of Science and Technology, 2012.
[25]	张盼. 基于混淆矩阵的分类器选择集成方法研究[D]. 焦作: 河南理工大学, 2016. Zhang P. Ensemble method of classifier selection based on confusion matrix[D]. Jiaozuo: Henan Polytechnic University, 2016.

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Image recognition of tree species based on multi feature fusion and CNN model