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Remote Sensing Letters Volume 13, 2022 - Issue 8
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Research Article

Sugarcane crop classification using time series analysis of optical and SAR sentinel images: a deep learning approach

Rahul Sreedhara Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, IndiaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-1411-831X
ORCID Icon,
Avnish Varshneyb India Meteorological Department, Ministry of Earth Sciences, New Delhi, IndiaCorrespondence[email protected]
&
M Dhanyaa Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India;c Department of Physics, Amrita Vishwa Vidyapeetham, Amritapuri, IndiaORCID Iconhttps://orcid.org/0000-0002-6958-2825
ORCID Icon
Pages 812-821 | Received 06 Mar 2022, Accepted 03 Jun 2022, Published online: 16 Jun 2022

Citations (3)

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Read on this site (1)

Wei Peng & Omid Karimi Sadaghiani. (2023) A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 45:3, pages 9178-9201.
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Articles from other publishers (2)

Haoran Meng, Cunjun Li, Yu Liu, Yusheng Gong, Wanying He & Mengxi Zou. (2023) Corn Land Extraction Based on Integrating Optical and SAR Remote Sensing Images. Land 12:2, pages 398.
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Dionysius Bryan Sencaki, Mega Novetrishka Putri, Hartanto Sanjaya, Hari Prayogi, Nico Anatoly, Afifuddin, Prabu Kresna Putra, Tiara Grace F.L & Muhammad Luthfi A.. (2022) Time Series Classification using Improved Deep Learning Approach for Agriculture Field Mapping. Time Series Classification using Improved Deep Learning Approach for Agriculture Field Mapping.

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