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

[1]	HUANG Motao, OUYANG Yongzhong, BIAN Shaofeng, LI Shanshan, LI Mingsan, LU Xiuping, WANG Weiping, DONG Chao, TANG Minqiang, HONG Lidan, HOU Guangchao. Analysis and Reflections on the Development of Underwater Gravity-Aided Inertial Navigation Technology in the United States and Russia[J]. Geomatics and Information Science of Wuhan University, 2024, 49(11): 1977-1991. DOI: 10.13203/j.whugis20240228
[2]	LI Qingquan, CHEN Ruizhe, TU Wei, CHEN Zhipeng, ZHANG Bochen, YAN Aiguo, YIN Pengcheng. Real-Time Vision-Based Deformation Measurement of Long-Span Bridge with Inertial Sensors[J]. Geomatics and Information Science of Wuhan University, 2023, 48(11): 1834-1843. DOI: 10.13203/j.whugis20230006
[3]	WU Yanxiong, TENG Yuntian, WU Qiong, XU Xing, ZHANG Bing. Error Correction Model and Uncertainty Analysis of the Shipborne Absolute Gravity Measurement System[J]. Geomatics and Information Science of Wuhan University, 2022, 47(4): 492-500. DOI: 10.13203/j.whugis20190412
[4]	ZHA Feng, HE Hongyang, LI Zhiwei, LI Jingshu. A SINS Initial Alignment Method Using Improved Parameter Identification[J]. Geomatics and Information Science of Wuhan University, 2020, 45(7): 974-979. DOI: 10.13203/j.whugis20180312
[5]	GUAN Bin, SUN Zhongmiao, WU Fumei, LIU Xiaogang. Influence of Horizontal Disturbing Gravity on Position Error in Inertial Navigation Systems[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1474-1481. DOI: 10.13203/j.whugis20160006
[6]	QIN Fangjun, LI An, XU Jiangning. Analysis of Errors of Rotating Modulation INS Effected by Angular Motion of Vehicle[J]. Geomatics and Information Science of Wuhan University, 2012, 37(7): 831-833.
[7]	QIN Fangjun, XU Jiangning, LI An. A New Calculative Method for Gyro-Free Inertial Navigation System Using 9Accelerometers[J]. Geomatics and Information Science of Wuhan University, 2012, 37(3): 278-281.
[8]	OUYANG Yongzhong, LU Xiuping, HUANG Motao, ZHAI Guojun. An Integrated Method for Compensating the Systematic Errors of Marine and Airborne Measurements from L&R Gravimeter[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5): 625-629.
[9]	JIN Jihang, BIAN Shaofeng. Analysis of Inertial Navigation System Positioning Error Caused by Gravity Disturbance[J]. Geomatics and Information Science of Wuhan University, 2010, 35(1): 30-32.
[10]	YANG Tao, WANG Wei, ZHU Zhiqin. Analysis and Verification of Time Synchronization Error in GPS/SINS Integrated System[J]. Geomatics and Information Science of Wuhan University, 2009, 34(10): 1181-1184.

Cited By

Get Citation

PDF

XML

Article views PDF downloads

Deep Learning and Remote Sensing Data Analysis

Abstract

References

Related Articles

Catalog

Related

Deep Learning and Remote Sensing Data Analysis

Abstract

References

Related Articles

Catalog

Related

Export File

Citation

Format

Content