Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2019, Vol. 34 Issue (4): 748-755    DOI: 10.11873/j.issn.1004-0323.2019.4.0748
    
Comparison Analysis on Wheat Mapping Using Deep Learning Algorithm from Different Satellite Data Source
Gang Cui(),Jinsheng Wu(),Zhen Yu,Ling Zhou
Survey Office of the National Bureau of Statistics in Shandong, Ji’nan, 250001, China
Download:  HTML  PDF (8929KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

Quantitative analysis on the relationships between the remote sensing scale and the land cover classification accuracy, which is the basis for making a decision on remote sensing resolution determination, is essential for mapping the concise land cover. Up to now, deep learning is an innovative algorithm to learn the hierarchical layer features without supervised control, which is different from the traditional classifiers that require man-made labels as input. Therefore, it is interesting to explore the inherent relationship between the classification accuracy and remote sensing image spatial scale from this algorithm. In this paper, we applied a Deep Convolutional Neural Network (DCNN) which is Pyramid Scene Parsing Network (PSPNet) on four scale remote sensing image (8 m, 3.2 m, 2 m, 0.8 m) to map the wheat distribution. The experiment results showed that the PSPNet is good at learning the spatial feature without manual operations, then the wheat extent could be extracted automatically. Different from the conventional algorithm of determining the optimized spatial resolution, the PSPNet could identify the wheat better accompanying with the spatial resolution increased and more concise wheat results could be obtained. This conclusions represent that deep convolution neural network can take full use of the spatial information of the high remote sensing image to ensure the performance of wheat extent, which brings us a new idea of improving the accuracy of crop mapping adequately if we can get the super-high resolution remote sensing image.

Key words:  Image fusion      Deep Convolution Neural Network      ResNet      PSPNet      GF-1/2     
Received:  10 May 2018      Published:  16 October 2019
ZTFLH:  S127  
Corresponding Authors:  Jinsheng Wu     E-mail:  cuigang@stats;wjs@stats
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Gang Cui
Jinsheng Wu
Zhen Yu
Ling Zhou

Cite this article: 

Gang Cui,Jinsheng Wu,Zhen Yu,Ling Zhou. Comparison Analysis on Wheat Mapping Using Deep Learning Algorithm from Different Satellite Data Source. Remote Sensing Technology and Application, 2019, 34(4): 748-755.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2019.4.0748     OR     http://www.rsta.ac.cn/EN/Y2019/V34/I4/748

Fig.1  Distribution of 19 villages
Fig.2  Winter wheat identification using PSPNet
Fig.3  Image and fusion image (subregion)
Fig.4  Variance of four remote sensing images
生产精度/%用户精度/%F1-Score

总体精度

/%

PSPNetGF-1-MUL0.800.880.8483.57
GF-1-GS0.840.890.8685.26
GF-2-MUL0.900.850.8789.12
GF-2-GS0.920.860.8990.78
SVMGF-1-MUL0.770.830.8080.29
GF-1-GS0.810.860.8385.26
GF-2-MUL0.880.850.8686.67
GF-2-GS0.880.830.8584.35
Table1  The accuracy of winter wheat identification for four remote sensing images
Fig.5  Results of winter wheat identification for four remote sensing images
1 MingDongping, WangQun, YangJianyu. Spatial Scale of Remote Sensing Image and Selection of Optimal Spatial Resolution[J]. Journal of Remote Sensing, 2008, 12(4): 529-537.
1 明冬萍,王群,杨建宇. 遥感影像空间尺度特性与最佳空间分辨率选择[J]. 遥感学报. 2008,12(4): 529-537.
2 MingDongping, QiuYufang, ZhouWen. Applying Spatial Statistics into Remote Sensing Pattern Recognition: with Case Study of Cropland Extraction based on GeOBIA[J]. Acta Geodaetica of Cartographica Sinica, 2016, 45(7): 825-833.
2 明冬萍, 邱玉芳, 周文.遥感模式分类中的空间统计学应用——以面向对象的遥感影像农田提取为例[J].测绘学报, 2016, 45(7): 825-833.
3 MarkhamB L, TownshendJ R G. Land Cover Classification Accuracy as a Function of Sensor Spatial Resolution[C]∥ Proceedings 15th Int. Symp.on Remote Sensing of Environment, Ann Arbor, MI, 1981.
4 MarceauD J, HayG J. Remote Sensing Contributions to the Scale Issue[J]. Canadian Journal of Remote Sensing, 1999, 25 (4): 357-366.
5 WoodcockC E, StrahlerA H. The Factor of Scale in Remote Sensing[J]. Remote Sensing of Environment, 1987, 21(3): 311-332.
6 MarkowitzH. Portfolio Selection-Efficient Diversification of Investments[M]. New York: Wiley, 1959.
7 ChenC L, WuG. Choice of Optimal Scale for Multi-source Remote Sensing Images[J]. Journal of Zhejiang A & F University, 2011,28(1): 164-172.
8 HanP, GongJ Y, LiZ L, et al. Selection of Optimal Scale in Remotely Sensed Image Classification[J]. Journal of Remote Sensing, 2010, 14(3): 507-518.
9 FengGuixiang, MingDongping. Fractal based Method on Selecting the Optimal Spatial Resolution for Remote Sensing Image[J]. Journal of Geo-Information Science, 2015, 17(4):478-495.
9 冯桂香, 明冬萍. 分形定量选择遥感影像最佳空间分辨率的方法与实验. 地理信息科学, 2015,17(4): 478-495.
10 YangYanjun, TianQingjiu, ZhanYulin, et al. Effects of Spatial Resolution and Texture Features on Multi-spectral Remote Sensing Classification[J]. Journal of Geo-information Science, 2018,20(1): 99-107.
10 杨闫君, 田庆久, 占玉林,等. 空间分辨率与纹理特征对多光谱遥感分类的影响. 地理信息科学, 2018,20(1): 99-107.
11 ReichsteinM, Camps-VallsG, StevensB, et al. Deep Learning and Process Understanding for Data-Driven Earth System Science[J]. Nature, 2019, 566(7743): 195-204.
12 MaggioriE, TarabalkaY, CharpiatG, et al. Convolutional Neural Networks for Large-scale Remote Sensing Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 55(2):645-657.
13 WeiY N, WangZ L, XuM. Road Structure Refined CNN for Road Extraction in Aerial Image[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(5):709-713.
14 ZhangBing. Remotely Sensed Big Data Era and Intelligent Information Extraction[J]. Geomatics and Information Science of Wuhan University, 2018, 53(12): 1861-1871.
14 张兵. 遥感大数据时代与智能信息提取[J]. 武汉大学学报(信息科学版), 2018, 53(12): 1861-1871.
15 ZhangKang, BaoqinHei, LiShengyang, et al. Complex Scene Classification of Remote Sensing Images based on CNN [J]. Remote Sensing for Land Resources, 2018, 33(6): 1095-1102.
15 张康, 黑保琴, 李盛阳,等. 基于CNN模型的遥感图像复杂场景分类[J]. 国土资源遥感, 2018, 33(6): 49-55.
16 YokoyaN, IwasakiA. Object Detection based on Sparse Representation and Hough Voting for Sptical Remote Sensing Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(5): 2053-2062.
17 XuKaijian, TianQingjiu, YangYanjun, et al. Response of Spatial Scale for Land Cover Classification of Remote Sensing[J]. Journal of Geo-Information Science, 2018,20(2):246-253.
17 徐凯健,田庆久,杨闫君,等.遥感土地覆被分类的空间尺度响应研究[J].地球信息科学学报,2018,20(2):246-253.
18 Zhao H S ShiJ P, QiX J, et al. Pyramid Scene Parsing Network[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 2881-2890.
19 KrizhevskyA, SutskeverI, HintonG E. ImageNet Classification with Deep Convolutional Neural Networks[C]∥ International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
20 SimonyanK, ZissermanA. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Computer Science, 2014:1409-1556.
21 SzegedyC, IoffeS, VanhouckeV, et al. Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning[C]∥ Thirty-First AAAI Conference on Artificial Intelligence, 2017, 4278-4284
22 HeK, ZhangX, RenS, et al. Deep Residual Learning for Image Recognition[C]∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.
23 YosinskiJ, CluneJ, BengioY, et al. How Transferable are Features in Deep Neural Networks?[C]∥ Advances in Neural Information Processing Systems, 2014: 3320-3328.
24 ZhouJingping, LiCunjun, ShiLeigang, et al. Crops Distribution Remote Sensing Extraction based on Decision Tree and Object-oriented Method[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(9): 318-326.
24 周静平,李存军,史磊刚,等. 基于决策树和面向对象的作物分布信息遥感提取[J]. 农业机械学报,2016,47(9):318-326.
25 WengYongling, TianQingjiu. Analysis and Evaluation of Method on Remote Sensing Date Fusion[J]. Remote Sensing Information,2003(3):49-54.
25 翁永玲,田庆久.遥感数据融合方法分析与评价综述[J].遥感信息,2003(3):49-54.
26 LiShuming, FengQuanlong, LiangQichun, et al. Aircraft Auto-detection in Domestic High Resolution Remote Sensing Images Using Deep-learning[J]. Remote Sensing Technology and Application, 2018, 33(6): 1095-1102.
26 李淑敏, 冯权泷, 梁其椿, 张学庆.基于深度学习的国产高分遥感影像飞机目标自动检测[J].遥感技术与应用, 2018, 33(6): 1095-1102.
No Suggested Reading articles found!