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Laser & Optoelectronics Progress, Volume. 56, Issue 7, 072801(2019)

Tree Species Classification and Mapping Based on Deep Transfer Learning with Unmanned Aerial Vehicle High Resolution Images

Wenxiu Teng1,2, Xiaorong Wen1,2、*, Ni Wang3,4, and Huihui Shi3
Author Affiliations
  • 1 Co-Innovation Center for the Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
  • 2 College of Forest, Nanjing Forestry University, Nanjing, Jiangsu 210037, China
  • 3 School of Geographic Information and Tourism, Chuzhou University, Chuzhou, Anhui 239000, China
  • 4 Anhui Engineering Laboratory of Geographical Information Intelligent Sensor and Service, Chuzhou, Anhui 239000, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (24)
    Cited By (5)
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    Figures & Tables(11)
    Model structure of convolutional neural network

    Fig. 1. Model structure of convolutional neural network

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    Comparison between traditional machine learning and transfer learning. (a) Traditional machine learning; (b) transfer learning

    Fig. 2. Comparison between traditional machine learning and transfer learning. (a) Traditional machine learning; (b) transfer learning

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    Flowchart of tree species classification and mapping of UAV high resolution image based on deep migration learning

    Fig. 3. Flowchart of tree species classification and mapping of UAV high resolution image based on deep migration learning

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    Tree species classification model

    Fig. 4. Tree species classification model

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    Flowchart of tree species mapping

    Fig. 5. Flowchart of tree species mapping

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    Tree species samples. (a) Metasequoia; (b) populus; (c) bamboo; (d) ginkgo

    Fig. 6. Tree species samples. (a) Metasequoia; (b) populus; (c) bamboo; (d) ginkgo

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    Training accuracy and loss curves of different models. (a) Training accuracy curves; (b) training loss curves

    Fig. 7. Training accuracy and loss curves of different models. (a) Training accuracy curves; (b) training loss curves

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    Confusion matrices of classification with different models. (a) VGG16; (b) ResNet50; (c) Inecption-v3; (d) Inception-ResNet-v2

    Fig. 8. Confusion matrices of classification with different models. (a) VGG16; (b) ResNet50; (c) Inecption-v3; (d) Inception-ResNet-v2

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    Training accuracy and loss curves of different methods. (a) Training accuracy curves; (b) training loss curves

    Fig. 9. Training accuracy and loss curves of different methods. (a) Training accuracy curves; (b) training loss curves

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    Confusion matrices of classification with different methods. (a) Small CNN; (b) proposed method

    Fig. 10. Confusion matrices of classification with different methods. (a) Small CNN; (b) proposed method

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    Tree species maps with different methods. (a) Original image; (b) actual map; (c) uniform decomposition method; (d) proposed method

    Fig. 11. Tree species maps with different methods. (a) Original image; (b) actual map; (c) uniform decomposition method; (d) proposed method

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    Wenxiu Teng, Xiaorong Wen, Ni Wang, Huihui Shi. Tree Species Classification and Mapping Based on Deep Transfer Learning with Unmanned Aerial Vehicle High Resolution Images[J]. Laser & Optoelectronics Progress, 2019, 56(7): 072801

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Oct. 8, 2018

    Accepted: Oct. 22, 2018

    Published Online: Jul. 30, 2019

    The Author Email: Wen Xiaorong (wenxiu_teng@163.com)

    DOI:10.3788/LOP56.072801

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology