激光与光电子学进展, 2020, 57 (10): 101001, 网络出版: 2020-05-08   

基于FCN的无人机可见光影像树种分类 下载: 1222次

Tree Species Identification Based on FCN Using the Visible Images Obtained from an Unmanned Aerial Vehicle
戴鹏钦 1,2,3,*丁丽霞 1,2,3,**刘丽娟 1,2,3董落凡 1,2,3黄依婷 3
作者单位
1 省部共建亚热带森林培育国家重点实验室, 浙江 杭州 311300
2 浙江省森林生态系统碳循环与固碳减排重点实验室, 浙江 杭州 311300
3 浙江农林大学环境与资源学院, 浙江 杭州 311300
引用该论文

戴鹏钦, 丁丽霞, 刘丽娟, 董落凡, 黄依婷. 基于FCN的无人机可见光影像树种分类[J]. 激光与光电子学进展, 2020, 57(10): 101001.

Pengqin Dai, Lixia Ding, Lijuan Liu, Luofan Dong, Yiting Huang. Tree Species Identification Based on FCN Using the Visible Images Obtained from an Unmanned Aerial Vehicle[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101001.

参考文献

[1] 滕文秀, 温小荣, 王妮, 等. 基于深度迁移学习的无人机高分影像树种分类与制图[J]. 激光与光电子学进展, 2019, 56(7): 072801.

    Teng W X, Wen X R, Wang N, et al. 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.

[2] 李夏, 吴开华, 巩永芳, 等. 休宁县主要树种生物量及碳储量分析[J]. 安徽农业大学学报, 2012, 39(4): 502-506.

    Li X, Wu K H, Gong Y F, et al. Forest biomass and forest carbon storage analysis for the main tree species in XiuningCounty[J]. Journal of Anhui Agricultural University, 2012, 39(4): 502-506.

[3] 余新晓, 鲁绍伟, 靳芳, 等. 中国森林生态系统服务功能价值评估[J]. 生态学报, 2005, 25(8): 2096-2102.

    Yu X X, Lu S W, Jin F, et al. The assessment of the forest ecosystem services evaluation in China[J]. Acta Ecologica Sinica, 2005, 25(8): 2096-2102.

[4] Ørka H O, Dalponte M, Gobakken T, et al. Characterizing forest species composition using multiple remote sensing data sources and inventory approaches[J]. Scandinavian Journal of Forest Research, 2013, 28(7): 677-688.

[5] Fassnacht F E, Latifi H, Stereńczak K, et al. Review of studies on tree species classification from remotely sensed data[J]. Remote Sensing of Environment, 2016, 186: 64-87.

[6] Jensen R R, Hardin P J, Hardin A J. Classification of urban tree species using hyperspectral imagery[J]. Geocarto International, 2012, 27(5): 443-458.

[7] 陈婷婷, 徐辉, 杨青, 等. 武夷山常绿阔叶林空间结构参数分布特征[J]. 生态学报, 2018, 38(5): 1817-1825.

    Chen T T, Xu H, Yang Q, et al. Spatial distribution characteristics of an evergreen broad-leaved forest in the Wuyi Mountains, Fujian Province, southeastern China[J]. Acta Ecologica Sinica, 2018, 38(5): 1817-1825.

[8] 张甍, 李婷婷, 张钦弟, 等. 太岳山主要树种空间分布格局及其维持机制研究[J]. 西北植物学报, 2017, 37(4): 782-789.

    Zhang M, Li T T, Zhang Q D, et al. Study on the spatial distribution patterns and maintaining mechanisms of dominant trees in Taiyue mountain, Shanxi[J]. Acta Botanica Boreali-Occidentalia Sinica, 2017, 37(4): 782-789.

[9] 张立保, 王鹏飞. 高分辨率遥感影像感兴趣区域快速检测[J]. 中国激光, 2012, 39(7): 0714001.

    Zhang L B, Wang P F. Fast detection of regions of interest in high resolution remote sensing image[J]. Chinese Journal of Lasers, 2012, 39(7): 0714001.

[10] Shang X, Chisholm L A. Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2481-2489.

[11] 欧攀, 张正, 路奎, 等. 基于卷积神经网络的遥感图像目标检测[J]. 激光与光电子学进展, 2019, 56(5): 051002.

    Ou P, Zhang Z, Lu K, et al. Object detectionin of remote sensing images based on convolutional neural networks[J]. Laser & Optoelectronics Progress, 2019, 56(5): 051002.

[12] 吴止锾, 高永明, 李磊, 等. 类别非均衡遥感图像语义分割的全卷积网络方法[J]. 光学学报, 2019, 39(4): 0428004.

    Wu Z H, Gao Y M, Li L, et al. Fully convolutional network method of semantic segmentation of class imbalance remote sensing images[J]. Acta Optica Sinica, 2019, 39(4): 0428004.

[13] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

[14] Han Y, Ye J C. Framing U-net via deep convolutional framelets: application to sparse-view CT[J]. IEEE Transactions on Medical Imaging, 2018, 37(6): 1418-1429.

[15] Xu Y Y, Wu L, Xie Z, et al. Building extraction in very high resolution remote sensing imagery using deep learning and guided filters[J]. Remote Sensing, 2018, 10(1): 144.

[16] 王露. 面向对象的高分辨率遥感影像多尺度分割参数及分类研究[D]. 长沙: 中南大学, 2014.

    WangL. Analysing classification and segmentation parameters selection in high resolution remote sensing image using based on object[D]. Changsha: Central South University, 2014.

[17] 张春晓, 侯伟, 刘翔, 等. 基于面向对象和影像认知的遥感影像分类方法: 以都江堰向峨乡区域为例[J]. 测绘通报, 2010( 4): 11- 14.

    Zhang CX, HouW, LiuX, et al. Remotesensing image classification based on object-oriented and image cognition: a case study in xiang'e, Dujiangyan[J]. Bulletin of Surveying and Mapping, 2010( 4): 11- 14.

[18] 刘兆祎, 李鑫慧, 沈润平, 等. 高分辨率遥感图像分割的最优尺度选择[J]. 计算机工程与应用, 2014, 50(6): 144-147.

    Liu Z Y, Li X H, Shen R P, et al. Selection of the best segmentation scale in high-resolution image segmentation[J]. Computer Engineering and Applications, 2014, 50(6): 144-147.

[19] Johnson B, Xie Z X. Unsupervised image segmentation evaluation and refinement using a multi-scale approach[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(4): 473-483.

[20] 汪小钦, 王苗苗, 王绍强, 等. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报, 2015, 31(5): 152-159.

    Wang X Q, Wang M M, Wang S Q, et al. Extraction of vegetation information from visible unmanned aerial vehicle images[J]. Transactions of the CSAE, 2015, 31(5): 152-159.

[21] 孙国祥, 汪小旵, 闫婷婷, 等. 基于机器视觉的植物群体生长参数反演方法[J]. 农业工程学报, 2014, 30(20): 187-195.

    Sun G X, Wang X C, Yan T T, et al. Inversion method of flora growth parameters based on machine vision[J]. Transactions of the CSAE, 2014, 30(20): 187-195.

[22] 任国贞, 江涛. 基于灰度共生矩阵的纹理提取方法研究[J]. 计算机应用与软件, 2014, 31(11): 190-192, 325.

    Ren G Z, Jiang T. Study on glcm-based texture extraction methods[J]. Computer Applications and Software, 2014, 31(11): 190-192, 325.

[23] Han N, Du H Q, Zhou G M, et al. Exploring the synergistic use of multi-scale image object metrics for land-use/land-cover mapping using an object-based approach[J]. International Journal of Remote Sensing, 2015, 36(13): 3544-3562.

[24] 顾星博, 温琪, 史晓雯, 等. 随机森林的并行运算方法及适用条件[J]. 实用预防医学, 2016, 23(2): 129-132.

    Gu X B, Wen Q, Shi X W, et al. Parallel random forest method and its applicable condition[J]. Practical Preventive Medicine, 2016, 23(2): 129-132.

[25] Breiman L, Breiman L, Cutler R. Random forests machine learning[J]. Journal of Clinical Microbiology, 2001, 2: 199-228.

[26] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.

[27] Otsu N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.

[28] 穆亚南, 丁丽霞, 李楠, 等. 基于面向对象和随机森林模型的杭州湾滨海湿地植被信息提取[J]. 浙江农林大学学报, 2018, 35(6): 1088-1097.

    Mu Y N, Ding L X, Li N, et al. Classification of coastal wetland vegetation in Hangzhou Bay with an object-oriented, random forest model[J]. Journal of Zhejiang A&F University, 2018, 35(6): 1088-1097.

[29] Hu Y F, Zhang Q L, Zhang Y Z, et al. A deep convolution neural network method for land cover mapping: a case study of Qinhuangdao, China[J]. Remote Sensing, 2018, 10(12): 2053.

[30] Rodriguez-Galiano V F, Ghimire B, Rogan J, et al. An assessment of the effectiveness of a random forest classifier for land-cover classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93-104.

戴鹏钦, 丁丽霞, 刘丽娟, 董落凡, 黄依婷. 基于FCN的无人机可见光影像树种分类[J]. 激光与光电子学进展, 2020, 57(10): 101001. Pengqin Dai, Lixia Ding, Lijuan Liu, Luofan Dong, Yiting Huang. Tree Species Identification Based on FCN Using the Visible Images Obtained from an Unmanned Aerial Vehicle[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101001.

本文已被 4 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!