Sugarcane crop classification using time series analysis of optical and SAR sentinel images: a deep learning approach-Reference-Cited by-同舟云学术

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

Published:2022-06-16 Issue:8 Volume:13 Page:812-821
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Sreedhar Rahul¹^ORCID,Varshney Avnish²,Dhanya M¹³^ORCID

Affiliation:

1. Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri, India

2. India Meteorological Department, Ministry of Earth Sciences, New Delhi, India

3. Department of Physics, Amrita Vishwa Vidyapeetham, Amritapuri, India

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2022.2088254

Reference17 articles.

1. Performance analysis of Backpropagation Algorithm of Artificial Neural Networks in Verilog

2. DeepAirNet: Applying Recurrent Networks for Air Quality Prediction

3. Identification of Sugarcane with NDVI Time Series Based on HJ-1 CCD and MODIS Fusion

4. Early Season Mapping of Sugarcane by Applying Machine Learning Algorithms to Sentinel-1A/2 Time Series Data: A Case Study in Zhanjiang City, China

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3. New Application Paradigm Of Time Series SAR Data For Sugarcane Mapping;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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