Rice recognition from Sentinel-1 SLC SAR data based on progressive feature screening and fusion

• Fuzzy rough feature sample screening under Gaussian statistical model. • Feature fusion model of rice growth height combined with MIMICs was constructed. • Feature fusion combined with BiLSTM network was used for rice extraction. Rice, a crucial global food crop, necessitates accurate mapping for food security assessment. China, a major rice producer and consumer, includes Jiangsu Province as a significant rice production region. The Hongzehu (HZH) area in Jiangsu contributes substantially to rice supply, supporting food security locally and province-wide. Sentinel-1 SAR data, particularly Single Look Complex (SLC) products, holds promise for precise crop mapping with enhanced phase and polarization information, enhancing sensitivity to rice growth changes by analyzing rice surface features information. However, challenges persist, especially climate impacts and timing inconsistencies between fields for planting rice. To overcome this, our study proposes a progressive feature screening and fusion method using multi-temporal SAR images. We introduce fuzzy coarse screening based on statistical distribution characteristics and refine it with Gaussian fitting. A model incorporating time-series sample separation and polarization decomposition feature fusion based on rice growth height enhances rice growth expression. For more precise results, we advocate a multi-temporal feature fusion approach using optimized sample features in the BiLSTM network to characterize rice growth and ground features. Experimental results demonstrate the method’s efficacy in two cities with a limited number of sampling points. The progressive feature fusion (DF) method outperforms classical classification methods using single feature (SF) or combined features (CF). Our proposed strategy proves effective for rice mapping applications, providing a promising approach for leveraging Sentinel-1 SLC SAR data. In conclusion, our study enhances accuracy in identifying rice fields and characterizing rice growth, contributing to improved food security assessments despite challenges associated with rainy seasons and planting times.