• Laser & Optoelectronics Progress
  • Vol. 56, Issue 7, 072801 (2019)
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
  • show less
    DOI: 10.3788/LOP56.072801 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    References

    [1] Plourde L C, Ollinger S V, Smith M L et al. Estimating species abundance in a northern temperate forest using spectral mixture analysis[J]. Photogrammetric Engineering & Remote Sensing, 73, 829-840(2007). http://www.ingentaconnect.com/content/asprs/pers/2007/00000073/00000007/art00005

    [2] Cho M A, Mathieu R, Asner G P et al. Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system[J]. Remote Sensing of Environment, 125, 214-226(2012). http://www.sciencedirect.com/science/article/pii/S0034425712002842

    [3] Dale V H, Joyce L A. McNulty S, et al. Climate change and forest disturbances[J]. BioScience, 51, 723-734(2001).

    [4] Thomas S C, Malczewski G. Wood carbon content of tree species in Eastern China: interspecific variability and the importance of the volatile fraction[J]. Journal of Environmental Management, 85, 659-662(2007). http://www.ncbi.nlm.nih.gov/pubmed/17187921

    [5] Gong P. Conifer species recognition: an exploratory analysis of in situ hyperspectral data[J]. Remote Sensing of Environment, 62, 189-200(1997). http://www.sciencedirect.com/science/article/pii/S0034425797000941

    [6] Waser L T, Ginzler C, Kuechler M et al. Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data[J]. Remote Sensing of Environment, 115, 76-85(2011). http://www.sciencedirect.com/science/article/pii/S0034425710002464

    [7] Fan C X, Han J, Xiong Z J et al. Application and status of unmanned aerial vehicle remote sensing technology[J]. Science of Surveying and Mapping, 34, 214-215(2009).

    [8] Le Louarn M, Clergeau P, Briche E et al. “Kill two birds with one stone”: urban tree species classification using bi-temporal Pléiades images to study nesting preferences of an invasive bird[J]. Remote Sensing, 9, 916(2017). http://www.cabdirect.org/cabdirect/abstract/20173339694

    [9] Dian Y Y, Pang Y, Dong Y F et al. Urban tree species mapping using airborne LiDAR and hyperspectral data[J]. Journal of the Indian Society of Remote Sensing, 44, 595-603(2016). http://link.springer.com/article/10.1007/s12524-015-0543-4

    [10] Ren C, Ju H B, Zhang H Q et al. Multi-source data for forest land type precise classification[J]. Scientia Silvae Sinicae, 52, 54-65(2016).

    [11] Chen Y, Fan R S, Wang J X et al. Segmentation of high-resolution remote sensing image combining phase consistency with watershed transformation[J]. Laser & Optoelectronics Progress, 54, 092803(2017).

    [12] Le Cun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

    [13] Shu C X, He Y T, Sun Q K. Point cloud registration based on convolutional neural network[J]. Laser & Optoelectronics Progress, 54, 031001(2017).

    [14] Yan Q, Li H, Jing L H et al. Automatic extraction algorithm of seismic landslide information based on after-calamity high-resolution remote sensing image[J]. Laser & Optoelectronics Progress, 54, 112801(2017).

    [15] Gong J Y, Ji S P. Photogrammetry and deep learning[J]. Acta Geodaetica et Cartographica Sinica, 47, 693-704(2018).

    [16] Deng J, Dong W, Socher R et al. ImageNet: a large-scale hierarchical image database. [C]∥IEEE Computer Vision and Pattern Recognition, 248-255(2009).

    [17] Pan S J, Yang Q. Asurvey on transfer learning[J]. IEEE Transactions on Knowledge and Data Engineering, 22, 1345-1359(2010). http://bib.oxfordjournals.org/external-ref?access_num=10.1109/TKDE.2009.191&link_type=DOI

    [18] Li L, Shu N. Object-oriented classification of high-resolution remote sensing image using structural feature. [C]∥International Congress on Image and Signal Processing, 2212-2215(2010).

    [19] Zhang F, Du B, Zhang L P. Scene classification via a gradient boosting random convolutional network framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1793-1802(2016). http://ieeexplore.ieee.org/document/7310864

    [20] Kermany D S, Goldbaum M, Cai W J et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 172, 1122-1131(2018). http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5?rss=yes

    [21] Yosinski J, Clune J, Bengio Y et al. How transferable are features in deep neural networks?. [C]∥International Conference on Neural Information Processing Systems, 3320-3328(2014).

    [22] Kornblith S, Shlens J[J]. Le Q V. Do better imagenet models transfer better?. arXiv, 08974, 2018(1805).

    [23] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012). http://www.ncbi.nlm.nih.gov/pubmed/22641706

    [24] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥International Conference on Neural Information Processing Systems, 1097-1105(2012).

    CLP Journals

    [1] Wenxiu Teng, Ni Wang, Taisheng Chen, Benlin Wang, Menglin Chen, Huihui Shi. Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images[J]. Laser & Optoelectronics Progress, 2019, 56(11): 112801

    [2] Wenxiu Teng, Ni Wang, Taisheng Chen, Benlin Wang, Menglin Chen, Huihui Shi. Deep Adversarial Domain Adaptation Method for Cross-Domain Classification in High-Resolution Remote Sensing Images[J]. Laser & Optoelectronics Progress, 2019, 56(11): 112801

    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
    Download Citation