Employing the Convolutional LSTM Network in Crop Classification using NDVI Time Series

Gili, Mohammad Reza; Ashourloo, Davoud; Aghighi, Hossein; Matkan, Ali Akbar; Shakiba, Alireza

doi:10.52547/gisj.14.1.89

Document Type : Original Article

Authors

¹ Ph.D. Student in Remote Sensing Center, Shahid Beheshti University

² Assistant Prof. of Remote Sensing Center, Faculty of Earth Sciences, Shahid Beheshti University

³ Prof. of Remote Sensing Center, Faculty of Earth Sciences, Shahid Beheshti University

⁴ Associate Prof. of Remote Sensing Center, Faculty of Earth Sciences, Shahid Beheshti University

https://doi.org/10.52547/gisj.14.1.89

Abstract

Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy and greenness during the growing season is one of the most prominent characteristics of vegetation, including agricultural products, which can be monitored by using time series of vegetation indices that have useful information about the sequence of phenological features of crops. The use of deep learning methods with the ability of learning sequential information obtained from these time series can be useful in crop mapping and reducing dependence on ground data. The LSTM network is one of the types of RNNs in sequential data analysis that has the ability to learn long-term sequences of time-series information. Therefore, in this study, after extracting the NDVI time-series of 9 different dates from Sentinel-2 satellite images for a region located in Moghan plain, with ground labeled data related to the type of crops cultivated, we trained a convolutional LSTM network. Then we used this trained network to classify agricultural products in another region of the plain as a test site, and achieved an overall accuracy of 82% and a kappa coefficient of 0.8. Increasing the number of ground samples and selecting the exact boundary of crops, can increase the efficiency of the method used.

Keywords

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Iranian Journal of Remote Sensing & GIS

Employing the Convolutional LSTM Network in Crop Classification using NDVI Time Series

References

References

Volume 14, Issue 1 - Serial Number 53
June 2022
Pages 89-106

Employing the Convolutional LSTM Network in Crop Classification using NDVI Time Series

References

References

Volume 14, Issue 1 - Serial Number 53June 2022Pages 89-106

Volume 14, Issue 1 - Serial Number 53
June 2022
Pages 89-106