[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).