Citation: | Ye Yang, Shen Bingyan, Shen Yuqi. Research on anti-shadow tree detection method based on generative adversarial network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(10): 118-126. DOI: 10.11975/j.issn.1002-6819.2021.10.014 |
[1] |
国家林业和草原局. 中国森林资源报告[M]. 北京:中国林业出版社,2019.
|
[2] |
王维刚,史海滨,李仙岳,等. 遥感订正作物种植结构数据对提高灌区SWAT模型精度的影响[J]. 农业工程学报,2020,36(17):158-166.Wang Weigang, Shi Haibin, Li Xianyue, et al. Effects of correcting crop planting structure data to improve simulation accuracy of SWAT model in irrigation district based on remote sensing[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(17): 158-166. (in Chinese with English abstract)
|
[3] |
李宇宸,张军,薛宇飞,等. 基于Google Earth Engine的中老缅交界区橡胶林分布遥感提取[J]. 农业工程学报,2020,36(8):174-181.Li Yuchen, Zhang Jun, Xue Yufei, et al. Remote sensing image extraction for rubber forest distribution in the border regions of China, Laos and Myanmar based on google earth engine platform[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(8): 174-181. (in Chinese with English abstract)
|
[4] |
赵晋陵,金玉,叶回春,等. 基于无人机多光谱影像的槟榔黄化病遥感监测[J]. 农业工程学报,2020,36(8):54-61.Zhao Jinling, Jin Yu, Ye Huichun, et al. Remote sensing monitoring of areca yellow leaf disease based on UAV multi-spectral images[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2020, 36(8): 54-61. (in Chinese with English abstract)
|
[5] |
Hellesen T, Matikainen L. An object-based approach for mapping shrub and tree cover on grassland habitats by use of LiDAR and CIR orthoimages[J]. Remote Sensing, 2013, 5(2): 558-583.
|
[6] |
Yang L, Wu X, Praun E, et al. Tree detection from aerial imagery[C]//Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, USA, 2009: 131-137.
|
[7] |
Pollock R J. The Automatic Recognition of Individual Trees in Aerial Images of Forests Based on a Synthetic Tree Crown Image Model[D]. Vancouver: The University of British Colombia, 1996: 172.
|
[8] |
Larsen M, Rudemo M. Optimizing templates for finding trees in aerial photographs[J]. Pattern Recognition Let, 1998, 19(12): 1153-1162.
|
[9] |
Novotny J, Hanu? J, Luke? P, et al. Individual tree crowns delineation using local maxima approach and seeded region-growing technique[C]//Proceedings of Symposium GIS Ostrava, Ostrava, Czech Republic, 2011: 27-39.
|
[10] |
郭昱杉,刘庆生,刘高焕,等. 基于标记控制分水岭分割方法的高分辨率遥感影像单木树冠提取[J]. 地球信息科学学报,2016,18(9):1259-1266.Guo Yushan, Liu Qingsheng, Liu Gaohuan, et al. Individual tree crown extraction of high resolution image based on marker-controlled watershed segmentation method[J]. Journal of Geo-information Science, 2016, 18(9): 1259-1266. (in Chinese with English abstract)
|
[11] |
Malek S, Bazi Y, Alajlan N, et al. Efficient framework for palm tree detection in UAV images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(12): 4692-4703.
|
[12] |
Windrim L, Bryson M. Detection, segmentation, and model fitting of individual tree stems from airborne laser scanning of forests using deep learning[J]. Remote Sensing, 2020, 12(9): 1469.
|
[13] |
Dai W, Yang B, Dong Z, et al. A new method for 3D individual tree extraction using multispectral airborne LiDAR point clouds[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144: 400-411.
|
[14] |
Marinelli D, Paris C, Bruzzone L. An approach to tree detection based on the fusion of multitemporal LiDAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(11): 1771-1775.
|
[15] |
Marselis S M, Tang H, Armston J, et al. Exploring the relation between remotely sensed vertical canopy structure and tree species diversity in Gabon[J]. Environmental Research Letters, 2019, 14(9): 1748-9326.
|
[16] |
Sainath T N, Mohamed A, Kingsbury B, et al. Deep convolutional neural networks for LVCSR[C]//IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 8614-8618.
|
[17] |
Le Q V, Ranzato M A, Monga R, et al. Building high-level features using large scale unsupervised learning[C]//In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013: 8595-8598.
|
[18] |
Zhu X, Tuia D, Mou L, et al. Deep learning in remote sensing: a comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8-36.
|
[19] |
Neupane B, Horanont T, Hung N D. Deep learning based banana plant detection and counting using high-resolution red-green-blue (RGB) images collected from unmanned aerial vehicle (UAV)[J]. PLoS ONE, 2019, 14(10): e0223906.
|
[20] |
Torres D L, Feitosa R Q, Happ P N, et al. Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery[J]. Sensors, 2020, 20(2): 563.
|
[21] |
Li W, Fu H, Yu L, et al. Deep learning-based oil palm tree detection and counting for high-resolution remote sensing images[J]. Remote Sensing, 2016, 9(1): 22.
|
[22] |
Guirado E, Tabik S, Alcaraz-Segura D, et al. Deep-learning versus OBIA for scattered shrub detection with google earth imagery: ziziphus lotus as case study[J]. Remote Sensing, 2017, 9(12): 1220.
|
[23] |
Culman M, Delalieux S, Tricht K V. Individual palm tree detection using deep learning on RGB imagery to support tree inventory[J]. Remote Sensing, 2020, 12(21): 3476.
|
[24] |
Ren S, He K, Girshick R, et al. Faster RCNN: towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing System, Vancouver, BC, Canada, 2015: 91-99.
|
[25] |
Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779-788.
|
[26] |
Ding B, Long C, Zhang L, et al. ARGAN: Attentive recurrent generative adversarial network for shadow detection and removal[C]//International Conference on Computer Vision (ICCV), Venice, Italy, 2020: 10212-10221.
|
[27] |
Zheng Q, Qiao X, Cao Y, et al. Distraction-aware shadow detection[C]//Conference on Computer Vision and Pattern Recognition (CVPR), California, USA, 2019: 5162-5171.
|
[28] |
Wang X, Shrivastava A, Gupta A. A-Fast-RCNN: hard positive generation via adversary for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, 2017: 2606-2615.
|
[29] |
Gougeon F. A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution images[J]. Canadian Journal of Remote Sensing, 1995, 21(3): 274-288.
|
[30] |
Erikson M. Segmentation of individual tree crowns in colour aerial photographs using region growing supported by fuzzy rules[J]. Canadian Journal of Forest Research, 2003, 33(8): 1557-1563.
|
[31] |
Lamar W R, McGraw J B, Warner T A. Multi-temporal censuring of a population of eastern hemlock (Tsuga canadensis L. ) from remotely sensed imagery using an automated segmentation and reconciliation procedure[J]. Remote Sensing of Environment, 2005, 94(1): 133-143.
|
[32] |
Larsen M, Eriksson M, Descombes X, et al. Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions[J]. International Journal of Remote Sensing, 2011, 32(20): 5827-5852.
|
[33] |
Dong T, Shen Y, Zhang J, et al. Progressive cascaded convolutional neural networks for single tree detection with google earth imagery[J]. Remote Sensing, 2019, 11(15): 1786.
|
[34] |
Foody G M. Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy[J]. Remote Sensing, 2004, 70: 627-633.
|