Spatial-temporal neural network for rice field classification from SAR Images

Chang, Yang-Lang and Tan, Tan-Hsu and Chen, Tsung-Hau and Chuah, Joon Huang and Chang, Lena and Wu, Meng-Che and Tatini, Narendra Babu and Ma, Shang-Chih and Alkhaleefah, Mohammad (2022) Spatial-temporal neural network for rice field classification from SAR Images. Remote Sensing, 14 (8). ISSN 2072-4292, DOI

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Agriculture is an important regional economic industry in Asian regions. Ensuring food security and stabilizing the food supply are a priority. In response to the frequent occurrence of natural disasters caused by global warming in recent years, the Agriculture and Food Agency (AFA) in Taiwan has conducted agricultural and food surveys to address those issues. To improve the accuracy of agricultural and food surveys, AFA uses remote sensing technology to conduct surveys on the planting area of agricultural crops. Unlike optical images that are easily disturbed by rainfall and cloud cover, synthetic aperture radar (SAR) images will not be affected by climatic factors, which makes them more suitable for the forecast of crops production. This research proposes a novel spatial-temporal neural network called a convolutional long short-term memory rice field classifier (ConvLSTM-RFC) for rice field classification from Sentinel-1A SAR images of Yunlin and Chiayi counties in Taiwan. The proposed model ConvLSTM-RFC is implemented with multiple convolutional long short-term memory attentions blocks (ConvLSTM Att Block) and a bi-tempered logistic loss function (BiTLL). Moreover, a convolutional block attention module (CBAM) was added to the residual structure of the ConvLSTM Att Block to focus on rice detection in different periods on SAR images. The experimental results of the proposed model ConvLSTM-RFC have achieved the highest accuracy of 98.08% and the rice false positive is as low as 15.08%. The results indicate that the proposed ConvLSTM-RFC produces the highest area under curve (AUC) value of 88% compared with other related models.

Item Type: Article
Funders: Ministry of Science and Technology, Taiwan [Grant No: MOST 110-2622-E-027-025 & 110-2119-M-027-001 & 110-2221-E-027-101 & 109-2116-M-027-004], National Space Organization [Grant No: NSPO-S-110244], National Science and Technology Center for Disaster Reduction [Grant No: NCDR-S-110096]
Uncontrolled Keywords: Convolutional long short-term memory network; Rice field classification; Sentinel-1A SAR images; Spatial-temporal neural network
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
T Technology > T Technology (General)
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 29 Sep 2023 02:16
Last Modified: 29 Sep 2023 02:16

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