遥感技术与应用 2019, Vol. 34 Issue (4): 704-711 DOI: 10.11873/j.issn.1004-0323.2019.4.0704 |
CNN 专栏 |
|
|
|
|
基于DenseNet的无人机光学图像树种分类研究 |
林志玮1,2,3( ),丁启禄1,黄嘉航2,涂伟豪1,胡典1,刘金福1,4 |
1. 福建农林大学计算机与信息学院, 福建 福州 350002 2. 福建农林大学林学院, 福建 福州 350002 3. 福建农林大学林学博士后流动站, 福建 福州 350002 4. 福建省高校生态与资源统计重点实验室, 福建 福州 350002 |
|
Study on Tree Species Classification of UAV Optical Image based on DenseNet |
Zhiwei Lin1,2,3( ),Qilu Ding1,Jiahang Huang2,Weihao Tu1,Dian Hu1,Jinfu Liu1,4 |
1. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China 2. College of Forestry, Fujian Agriculture and Forestry University, Fuzhou 350002, China 3. Forestry Post-doctoral station of Fujian Agriculture and Forestry University Key Laboratory for Ecology and Resource Statistics of Fujian Province, Fuzhou 350002, China 4. Key Laboratory for Ecology and Resource Statistics of Fujian Province, Fuzhou 350002, China |
引用本文:
林志玮,丁启禄,黄嘉航,涂伟豪,胡典,刘金福. 基于DenseNet的无人机光学图像树种分类研究[J]. 遥感技术与应用, 2019, 34(4): 704-711.
Zhiwei Lin,Qilu Ding,Jiahang Huang,Weihao Tu,Dian Hu,Jinfu Liu. Study on Tree Species Classification of UAV Optical Image based on DenseNet. Remote Sensing Technology and Application, 2019, 34(4): 704-711.
链接本文:
http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.4.0704
或
http://www.rsta.ac.cn/CN/Y2019/V34/I4/704
|
1 |
Liu Xusheng , Zhao Xiaoli . Research Advances and Countermeasures of Remote Sensing Classification of Forest Vegetation[J]. Forest Resources Management, 2004(1):61-64.
|
1 |
刘旭升,张晓丽 .森林植被遥感分类研究进展与对策[J].林业资源管理,2004(1):61-64.
|
2 |
Zhang Chenkun , Han Min . Spectral-spatial Joint Classification of Hyperspectral Image with Edge-preserving Filtering[J]. Acta Automatica Sinica, 2018, 44(2): 280-288.
|
2 |
张成坤, 韩敏 . 基于边缘保持滤波的高光谱影像光谱-空间联合分类[J]. 自动化学报, 2018, 44(2): 280-288.
|
3 |
Liu Xiuying , Lin Hui , Xiong Jianli , et al .Band Selection from Hyperspectral Data of Forestry Species [J]. Remote Sensing Information,2005(4):41-44.
|
3 |
刘秀英,林辉,熊建利,等 .森林树种高光谱波段的选择[J].遥感信息,2005(4):41-44.
|
4 |
Wang Zhihui , Ding Lixia . Tree Species Discrimination based on Leaf-level Hyperspectral Characteristic Analysis[J]. Spectroscopy and Spectral Analysis,2010,30(7):1825-1829.
|
4 |
王志辉,丁丽霞 .基于叶片高光谱特性分析的树种识别[J].光谱学与光谱分析,2010,30(7):1825-1829.
|
5 |
Chen Zhuo , Ma Hongchao .Automatic Extracting and Modeling Approach of City Cloverleaf from Airborne LiDAR Data[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 252-258.
|
5 |
陈卓,马洪超 . 基于机载LiDAR数据的大型立交桥自动提取与建模方法[J]. 测绘学报, 2012, 41(2): 252-258.
|
6 |
Liu Lihuan , Pang Yong , Fan Yiwen , et al .Fused Airborne Li DAR and Hyperspectral Data for Tree Species Identification in a Natural Temperate Forest[J]. Journal of Remote Sensing,2013,17(3):679-695.
|
6 |
刘丽娟,庞勇,范文义,等 .机载LiDAR和高光谱融合实现温带天然林树种识别[J].遥感学报,2013,17(3):679-695.
|
7 |
Kwak D A , Lee W K , Lee J H , et al . Detection of Individual Trees and Estimation of Tree Height Using LiDAR Data[J]. Journal of Forest Research, 2007, 12(6):425-434.
|
8 |
Chen Jianming , Chen Zhibo , Yang Meng , et al .Research on Tree Species Identification Algorithm based on Combination of Leaf Traditional Characteristics and Distance Matrix as well as Corner Matrix[J]. Journal of Beijing Forestry University,2017,39(2):108-116.
|
8 |
陈明健,陈志泊,杨猛,等 .叶片传统特征和距离矩阵与角点矩阵相结合的树种识别算法[J].北京林业大学学报,2017,39(2):108-116.
|
9 |
Iwata T , Saitoh T .Tree Recognition based on Leaf Images[C]∥IEEE, Sice Conference. 2014:2489-2494.
|
10 |
Krizhevsky A , Sutskever I , Hinton G E . ImageNet Classification with Deep Convolutional Neural Networks[C]∥International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
|
11 |
Simonyan K , Zisserman A . Very Deep Convolutional Networks for Large-scale Image Recognition[J]. arXiv preprint arXiv: 1409.1556, 2014.
|
12 |
Szegedy C , Liu W , Jia Y , et al . Going Deeper with Convolutions[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1-9.
|
13 |
He K , Zhang X , Ren S , et al . Deep Residual Learning for Image Recognition[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
|
14 |
Huang G , Liu Z , Maaten V D , et al . Densely Connected Convolutional Networks[C]⫽ arXiv Preprint arXiv: 1608.06993, 2016.
|
15 |
O'Shea K , Nash R . An Introduction to Convolutional Neural Networks[J]. arXiv preprint arXiv: 1511.08458, 2015.
|
16 |
Zeiler M D , Fergus R . Visualizing and Understanding Convolutional Networks[C]∥European Conference on Computer Vision. Springer, Cham, 2014: 818-833.
|
17 |
Luo W , Li Y , Urtasun R , et al . Understanding the Effective Receptive Field in Deep Convolutional Neural Networks[C]∥Advances in Neural Information Processing Systems. 2016: 4898-4906.
|
18 |
Geng M , Wang Y , Xiang T , et al . Deep Transfer Learning for Person Re-identification[J]. arXiv Preprint arXiv: 1611.05244, 2016.
|
19 |
Shao L , Zhu F , Li X . Transfer Learning for Visual Categorization: A Survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(5): 1019-1034.
|
20 |
Xie M , Jean N , Burke M , et al . Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping[J]. arXiv Preprint arXiv: 1510.00098, 2015.
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|