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.
Full text not available from this repository.Abstract
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.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | National Science and Technology Center for Disaster Reduction [Grant no. NCDR-S-110096], Ministry of Science and Technology, Taiwan [Grant no. 109-2116-M-027-004, 110-2119-M-027-001, 110-2221-E-027-101, 110-2622-E-027-025], National Space Organization [Grant no. NSPO-S-110244] |
| Uncontrolled Keywords: | Spatial temporal neural network; Synthetic aperture radar images; Sentinel-1A; Rice field classification |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 13 Feb 2025 07:02 |
| Last Modified: | 13 Feb 2025 07:02 |
| URI: | http://eprints.um.edu.my/id/eprint/40465 |
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