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: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/40465/ |
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| Summary: | 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. |
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