Staff View: Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images

Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images

Taiwan's agriculture is an important national economic industry. Ensuring food security and stabilizing the food supply are the government's primary goals. The Agriculture and Food Agency (AFA) of the Executive Yuan's Council of Agriculture has conducted agricultural and food surveys...

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Main Authors:	Chang, Yang-Lang, Tatini, Narendra Babu, Chen, Tsung-Hau, Wu, Meng-Che, Chuah, Joon Huang, Chen, Yi-Ting, Chang, Lena
Format:	Conference or Workshop Item
Published:	IEEE 2022
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/40465/
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spelling	my.um.eprints.404652025-02-13T07:02:25Z http://eprints.um.edu.my/40465/ Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images Chang, Yang-Lang Tatini, Narendra Babu Chen, Tsung-Hau Wu, Meng-Che Chuah, Joon Huang Chen, Yi-Ting Chang, Lena TK Electrical engineering. Electronics Nuclear engineering Taiwan's agriculture is an important national economic industry. Ensuring food security and stabilizing the food supply are the government's primary goals. The Agriculture and Food Agency (AFA) of the Executive Yuan's Council of Agriculture has conducted agricultural and food surveys to address those issues. Synthetic aperture radar (SAR) images will not be affected by climatic factors, which makes them more suitable for the forecast of rice production. This research uses the spatial-temporal neural network convolutional long short-term memory network (ConvLSTM) to identify rice fields from SAR images. The results show that ConvLSTM can greatly reduce the proportion of model false positives to 51.16%, produced higher average precision of 95.70%, and F1-score of 0.9648. The ConvLSTM neural network has produced good results for rice field identification compared with state-of-the-art neural networks. IEEE 2022 Conference or Workshop Item PeerReviewed Chang, Yang-Lang and Tatini, Narendra Babu and Chen, Tsung-Hau and Wu, Meng-Che and Chuah, Joon Huang and Chen, Yi-Ting and Chang, Lena (2022) Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images. In: 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022, 17-22 July 2022, Kuala Lumpur.
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Chang, Yang-Lang Tatini, Narendra Babu Chen, Tsung-Hau Wu, Meng-Che Chuah, Joon Huang Chen, Yi-Ting Chang, Lena Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images
description	Taiwan's agriculture is an important national economic industry. Ensuring food security and stabilizing the food supply are the government's primary goals. The Agriculture and Food Agency (AFA) of the Executive Yuan's Council of Agriculture has conducted agricultural and food surveys to address those issues. Synthetic aperture radar (SAR) images will not be affected by climatic factors, which makes them more suitable for the forecast of rice production. This research uses the spatial-temporal neural network convolutional long short-term memory network (ConvLSTM) to identify rice fields from SAR images. The results show that ConvLSTM can greatly reduce the proportion of model false positives to 51.16%, produced higher average precision of 95.70%, and F1-score of 0.9648. The ConvLSTM neural network has produced good results for rice field identification compared with state-of-the-art neural networks.
format	Conference or Workshop Item
author	Chang, Yang-Lang Tatini, Narendra Babu Chen, Tsung-Hau Wu, Meng-Che Chuah, Joon Huang Chen, Yi-Ting Chang, Lena
author_facet	Chang, Yang-Lang Tatini, Narendra Babu Chen, Tsung-Hau Wu, Meng-Che Chuah, Joon Huang Chen, Yi-Ting Chang, Lena
author_sort	Chang, Yang-Lang
title	Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images
title_short	Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images
title_full	Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images
title_fullStr	Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images
title_full_unstemmed	Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images
title_sort	convlstm neural network for rice field classification from sentinel-1a sar images
publisher	IEEE
publishDate	2022
url	http://eprints.um.edu.my/40465/
_version_	1825160579555262464
score	13.239859

Convlstm Neural Network for Rice Field Classification from Sentinel-1A Sar Images

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