[1]Dominique C,Christopher D,Francis C M . An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery[J]. Avian Conservation and Ecology,2018,13(1):15.
[2]Descamps S,Bechet A,Descombes X,et al. An automatic counter for aerial images of aggregations of large birds[J]. Bird Study,2011,58(3):302-308.
[3]Geoff G B,Michael S,Rasmus D N,et al. Remote sensing image data and automated analysis to describe marine bird distributions and abundances[J]. Ecological Informatics,2013,14:2-8.
[4]Rey N,Volpi M,Joost S,et al. Detecting animals in African Savanna with UAVs and the crowds[J]. Remote Sensing of Environment,2017,200:341-351.
[5]Fretwell P T,Scofield P,Phillips R A . Using super‐high resolution satellite imagery to census threatened albatrosses[J]. Ibis,2017,159(3):481-490.
[6]Hollings T,Burgman M,van Andel M,et al. How do you find the green sheep? A critical review of the use of remotely sensed imagery to detect and count animals[J]. Methods in Ecology & Evolution,2018,9(4):881-892.
[7]Christiansen P M,Steen K A,Rgensen R N,et al. Automated detection and recognition of wildlife using thermal cameras[J]. Sensors,2014,14(8):13778-13793.
[8]Liu C C,Chen Y H,Wen H L . Supporting the annual international black-faced spoonbill census with a low-cost unmanned aerial vehicle[J]. Ecological Informatics,2015,30:170-178.
[9]Seymour A C,Dale J,Hammill M,et al. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery[J]. Entific Reports,2017,7(1):45127.
[10]Ripple L W J . Automated wildlife counts from remotely sensed imagery[J]. Wildlife Society Bulletin,2003,31(2):362-371.
[11]Luis G,Glen M,Eduard P,et al. Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation[J]. Sensors,2016,16(1):97.
[12]Fretwell P T,Staniland I J,Forcada J. Whales from space:countingsouthern right whales by satellite[J]. PLoS One,2014,9(2):e88655.
[13]Yang Z,Wang T,Skidmore A K,et al. Spotting east African mammals in open savannah from space[J]. PLoS One,2014,9(12):16.
[14]Terletzky P,Ramsey R D. A semi-automated single day image differencing technique to identify animals in aerial imagery[J]. Plos One,2014,9(1):e85239.
[15]Longmore S N,Collins R P,Pfeifer S,et al. Adapting astronomical source detection software to help detect animals in thermal images obtained by unmanned aerial systems[J]. International Journal of Remote Sensing,2017,38(8/9/10):2623-2638.
[16]Olivares-Mendez M,Fu C H,Ludivig P,et al. Towards an autonomous vision-basedunmanned aerial system against wildlife poachers[J]. Sensors 2015,15(12):31362-31391.
[17]Torney C J,Dobson A P,Borner F,et al. Assessing rotation-invariant feature classification for automated wildebeest population Counts[J]. PLoS One,2016,11(5):e0156342.
[18]Xue Y F,Wang T J,Skidmore A K. Automatic counting of large mammalsfrom very high resolution panchromatic satellite imagery[J]. Remote Sensing,2017,9(9):1-16.
[19]Lecun Y,Bengio Y,Hinton G E. Deep learning[J]. Nature,2015,521(7553):436-444.
[20]Reichstein M,Camps valls G,Stevens B,et al. Deep learning and process understanding for data-driven Earth system science[J]. Nature,2019,566(7743):195-204.
[21]Waldrop M M . News feature:what are the limits of deep learning?[J]. Proceedings of the National Academy of Sciences,2019,116(4):1074-1077.
[22]Zhu X X,Tuia D,Mou L,et al. Deep learning in remote sensing:a comprehensive review and list of resources[J]. IEEE Geoence & Remote Sensing Magazine,2018,5(4):8-36.
[23]Benjamin K,Diego M,Devis T. Detecting mammals in UAV images:best practices to address a substantially imbalanced dataset with deep learning[J]. Remote Sensing of Environment,2018,216:139-153.
[24]Norouzzadeh M S,Nguyen A,Kosmala M,et al. Automatically identifying,counting,and describing wild animals in camera-trap images with deep learning[J]. Proceedings of the National Academy of Sciences of the United States of America,2018,115(25):5716-5725.
[25]邵全琴,樊江文. 三江源区生态系统综合监测与评估[M]. 北京:科学出版社,2012.
[26]罗巍,王东亮,夏列钢,等. 一种基于深度学习的林业资源调查方法[J]. 林业科技通讯,2020(8):17-22.
[1]刘伟,赵庆展,汪传建,等.基于最小二乘支持向量机的无人机遥感影像分类[J].江苏农业科学,2017,45(09):187.
Liu Wei,et al.Remote sensing image classification of UAV based on least squares support vector machine[J].Jiangsu Agricultural Sciences,2017,45(20):187.