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Sensors
Volume 19
Issue 19
10.3390/s19194227
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Article

Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data

by
Yingwei Sun
1,2,
Jiancheng Luo
1,2,*,
Tianjun Wu
3,
Ya’nan Zhou
4,
Hao Liu
1,2,
Lijing Gao
2,
Wen Dong
2,
Wei Liu
1,2,
Yingpin Yang
1,2,
Xiaodong Hu
2,
Lingyu Wang
5 and
Zhongfa Zhou
5
1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710064, China
4
School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China
5
College of Geography and Environmental Sciences, Guizhou Normal University, Guiyang 550001, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4227; https://doi.org/10.3390/s19194227
Received: 25 July 2019 / Revised: 26 September 2019 / Accepted: 27 September 2019 / Published: 28 September 2019
(This article belongs to the Section Remote Sensors)
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Abstract

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future. View Full-Text
Keywords: optical time-series data; SAR time-series data; RNN; synchronous response relationship; cloudy and rainy region; crop classification optical time-series data; SAR time-series data; RNN; synchronous response relationship; cloudy and rainy region; crop classification
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MDPI and ACS Style

Sun, Y.; Luo, J.; Wu, T.; Zhou, Y.; Liu, H.; Gao, L.; Dong, W.; Liu, W.; Yang, Y.; Hu, X.; Wang, L.; Zhou, Z. Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data. Sensors 2019, 19, 4227. https://doi.org/10.3390/s19194227

AMA Style

Sun Y, Luo J, Wu T, Zhou Y, Liu H, Gao L, Dong W, Liu W, Yang Y, Hu X, Wang L, Zhou Z. Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data. Sensors. 2019; 19(19):4227. https://doi.org/10.3390/s19194227

Chicago/Turabian Style

Sun, Yingwei, Jiancheng Luo, Tianjun Wu, Ya’nan Zhou, Hao Liu, Lijing Gao, Wen Dong, Wei Liu, Yingpin Yang, Xiaodong Hu, Lingyu Wang, and Zhongfa Zhou. 2019. "Synchronous Response Analysis of Features for Remote Sensing Crop Classification Based on Optical and SAR Time-Series Data" Sensors 19, no. 19: 4227. https://doi.org/10.3390/s19194227

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