Deep Learning and Remote Sensing Data Analysis

ZHANG Liqiang; LI Yang; HOU Zhengyang; LI Xingang; GENG Hao; WANG Yuebin; LI Jingwen; ZHU Panpan; MEI Jie; JIANG Yanxiao; LI Shuaipeng; XIN Qi; CUI Ying; LIU Suhong

doi:10.13203/j.whugis20200650

Volume 45 Issue 12

Dec. 2020

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2020 > 45(12): 1857-1864. > DOI: 10.13203/j.whugis20200650

ZHANG Liqiang, LI Yang, HOU Zhengyang, LI Xingang, GENG Hao, WANG Yuebin, LI Jingwen, ZHU Panpan, MEI Jie, JIANG Yanxiao, LI Shuaipeng, XIN Qi, CUI Ying, LIU Suhong. Deep Learning and Remote Sensing Data Analysis[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650

Citation:

PDF (9311 KB)

Deep Learning and Remote Sensing Data Analysis

1.
Department of Geographical Sciences, Beijing Normal University, Beijing 100875, China
2.
School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
3.
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China

Funds:

The National Natural Science Foundation of China 41925006

More Information

Author Bio:
ZHANG Liqiang, PhD, professor, specializes in spatial analysis and remote sensing data processing.E-mail: zhanglq@bnu.edu.cn
Received Date: November 30, 2020
Published Date: December 04, 2020

Graphical Abstract

Abstract

Abstract

The rapid development of deep learning provides an important technical means for intelligent analysis of remote sensing big data. Firstly, this paper mainly introduces the deep learning modes in remote sensing data recognition and application, and proposes a deep reinforcement learning, multi-task learning and sub-pixel-pixel-super-pixel feature learning models for object features recognition from LiDAR point clouds, optical remote sensing images and hyperspectral images. The model parameters are basically obtained by learning, and thus the workload of the parameter adjustments is small. The spatial and contextual information, texture and spectral characteristics between ground objects are fully taken into account, so the presented models have good generalization abilities. Then, it describes the progress in terms of the joint deep learning and multi-source remote sensing data in accurate poverty alleviation assessment, wetland change and spatial analysis in Qinghai-Tibet Plateau in the past 20 years, and corn yield estimation. In order to better promote the transformation from remote sensing data to knowledge, it is necessary give full play to the advantages of deep learning in remote sensing big data processing, and develop new data processing algorithms and technologies.
- deep reinforcement learning,
- multi-source remote sensing data,
- object recognition

FullText(HTML)

References (21)

References

[1]	李德仁, 张良培, 夏桂松.遥感大数据自动分析与数据挖掘[J].测绘学报, 2014, 43(12): 1 211-1 216 http://www.cnki.com.cn/Article/CJFDTotal-CHXB201412002.htm Li Deren, Zhang Liangpei, Xia Guisong. Automatic Analysis and Mining of Remote Sensing Big Data[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(12): 1 211-1 216 http://www.cnki.com.cn/Article/CJFDTotal-CHXB201412002.htm
[2]	Markus R, Gustau C, Bjorn S, et al.Deep Learning and Process Understanding for Data-Driven Earth System Science[J]. Nature, 2019, 566: 195-204 doi: 10.1038/s41586-019-0912-1
[3]	Sun C, Shrivastava A, Singh1 S, et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era[C].IEEE International Conference on Computer Vision, Venice, Italy, 2017
[4]	Liu Fangyu, Li Shuaipeng, Zhang Liqiang, et al.3DCNN-DQN-RNN: A Deep Reinforcement Learning Framework for Semantic Parsing of Large-scale 3D Point Clouds[C].IEEE International Conference on Computer Vision, Venice, Italy, 2017
[5]	Bischke B, Helber P, Folz J, et al.Multi-task Learning for Segmentation of Building Footprints with Deep Neural Networks[C].IEEE International Conference on Image Processing, Taipei, China, 2019
[6]	He K, Gkioxari G, Dollar P, et al. Mask R-CNN[C]. IEEE International Conference on Computer Vision, Venice, Italy, 2017
[7]	Lin T Y, Dollár P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017
[8]	Bertelli L, Yu T, Vu D, et al. Kernelized Structural SVM Learning for Supervised Object Segmentation[J]. Communications in Computer & Information Science, 2011, 32(14):2 153-2 160
[9]	Yang J, Yang M H. Top-down Visual Saliency via Joint CRF and Dictionary Learning[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(3): 576-588 doi: 10.1109/TPAMI.2016.2547384
[10]	Mei J, Wang Y, Zhang L, et al.PSASL:Pixel-Level and Superpixel-Level Aware Subspace Learning for Hyperspectral Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7):1-16 doi: 10.1109/TGRS.2019.2923175
[11]	Lee H, Kwon H.Going Deeper with Contextual CNN for Hyperspectral Image Classification[J]. IEEE Transactions on Image Processing, 2017, 26(10): 4 843-4 855 doi: 10.1109/TIP.2017.2725580
[12]	Zhong P, Gong Z, Li S, et al.Learning to Diversify Deep Belief Networks for Hyperspectral Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6):3 516-3 530 doi: 10.1109/TGRS.2017.2675902
[13]	薛澜, 翁凌飞.中国实现联合国2030年可持续发展目标的政策机遇和挑战[J].中国软科学, 2017, 1: 1-12 http://www.cnki.com.cn/Article/CJFDTotal-ZGRK201701001.htm Xue Lan, Weng Lingfei.The Policy Opportunities and Challenges in China's Implementation of 2030 Sustainable Development Goals[J].China Soft Science, 2017, 1: 1-12 http://www.cnki.com.cn/Article/CJFDTotal-ZGRK201701001.htm
[14]	林万达.从数据造假现象讨论微妙的央地经济关系及其重塑与调整[J].中国市场, 2018, 973(18): 28-30 http://www.cnki.com.cn/Article/CJFDTotal-SCZG201818007.htm Lin Wanda. Discussion on Subtle Central-local Economic Relations and Their Reshaping and Adjustment from the Phenomenon of Data Falsification[J].China Market, 2018, 973(18): 28-30 http://www.cnki.com.cn/Article/CJFDTotal-SCZG201818007.htm
[15]	王欢, 郑旷怡.部分省份近期调整统计数据的原因简析.中国市场, 2018, 36: 10-12 http://www.cnki.com.cn/Article/CJFDTotal-SCZG201836003.htm Wang Huan. Zheng Kuangyi. Analysis for Recent Adjustment of Statistical Data in Some Provinces[J]. China Market, 2018, 36: 10-12 http://www.cnki.com.cn/Article/CJFDTotal-SCZG201836003.htm
[16]	Yeh C, Perez A, Driscoll A, et al. Using Publicly Available Satellite Imagery and Deep Learning to Understand Economic Well-Being in Africa[J]. Nature Communications, 2020, 11:2 583 doi: 10.1038/s41467-020-16185-w
[17]	Jean N, Burke M, Xie M, et al. Combining Satellite Imagery and Machine Learning to Predict Poverty[J]. Science, 2016, 353(6 301):790-794 doi: 10.1126/science.aaf7894
[18]	Zhao Z, Zhang Y, Liu L, et al. Recent Changes in Wetlands on the Tibetan Plateau:A Review[J].Journal of Geographical Sciences, 2015, 25(7): 879-896 doi: 10.1007/s11442-015-1208-5
[19]	Pekel J F, Cottam A, Gorelick N, et al.High-Resolution Mapping of Global Surface Water and Its Long-Term Changes[J].Nature, 2016, 540(7 633): 418-422 doi: 10.1038/nature20584
[20]	Mao D, Wang Z, Du B, et al. National Wetland Mapping in China: A New Product Resulting from Object-Based and Hierarchical Classification of Landsat 8 OLI Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 164: 11-25 doi: 10.1016/j.isprsjprs.2020.03.020
[21]	Lei Y, Zhu Y, Wang B, et al. Extreme Lake Level Changes on the Tibetan Plateau Associated with the 2015/2016 El Niño[J]. Geophysical Research Letters, 2019, 46(11): 5 889-5 898 doi: 10.1029/2019GL081946

Cited By

Cited by

Periodical cited type(8)

1.	高奎亮，刘冰，余旭初，余岸竹，孙一帆. 面向高光谱影像分类的网络结构自动搜索方法. 武汉大学学报(信息科学版). 2024(02): 225-235 .
2.	崔林林，仙巍，柳锦宝. 产学研背景下遥感科学与技术专业”遥感原理与应用”实验教学改革探讨. 电脑与信息技术. 2023(01): 92-95 .
3.	李杰，曾超，刘汇慧，李慧芳. 地学大数据背景下遥感课程的多阶段进阶混合式教学模式探索. 测绘通报. 2023(S2): 131-136 .
4.	薛冰，赵冰玉，李京忠. 地理学视角下城市复杂性研究综述——基于近20年文献回顾. 地理科学进展. 2022(01): 157-172 .
5.	桑国庆，唐志光，毛克彪，邓刚，王靖文，李佳. 基于GEE云平台与Sentinel数据的高分辨率水稻种植范围提取——以湖南省为例. 作物学报. 2022(09): 2409-2420 .
6.	徐恩恩，郭颖，陈尔学，李增元，赵磊，刘清旺. 基于无人机LiDAR和高空间分辨率卫星遥感数据的区域森林郁闭度估测模型. 武汉大学学报(信息科学版). 2022(08): 1298-1308 .
7.	孙玉梅，刘昱豪，边占新，孙亮，陈敬周. 深度学习PaddlePaddle框架支持下的遥感智能视觉平台研究与实现. 测绘通报. 2021(11): 65-69+75 .
8.	陈晓峰. 光学遥感立体测绘技术及其发展趋势研究. 光源与照明. 2021(11): 69-71 .

Other cited types(8)

Get Citation

PDF

XML

Article views (4726) PDF downloads (823) Cited by(16)

Deep Learning and Remote Sensing Data Analysis

Abstract

References

Cited by

Periodical cited type(8)

Other cited types(8)

Catalog

Related

Deep Learning and Remote Sensing Data Analysis

Abstract

References

Cited by

Periodical cited type(8)

Other cited types(8)

Catalog

Related

Export File

Citation

Format

Content