Work

Optical and SAR Data Fusion Based on Transformer for Rice Identification: A Comparative Analysis from Early to Late Integration

Year: 2025

Type: article

Abstract: The accurate identification of rice fields through remote sensing is critical for agricultural monitoring and global food security. While optical and Synthetic Aperture Radar (SAR) data offer complementary advantages for crop mapping—spectral richness from optical imagery and all-weather capabilities from SAR—their integration remains challenging due to heterogeneous data characteristics and environmental variability. This study systematically evaluates three Transformer-based fusion strategies for rice identification: Early Fusion Transformer (EFT), Feature Fusion Transformer (FFT), and Decision Fusion Transformer (DFT), designed to integrate optical-SAR data at the input level, feature level, and decision level, respectively. Experiments conducted in Arkansas, USA—a major rice-producing region with complex agroclimatic conditions—demonstrate that EFT achieves superior performance, with an overall accuracy (OA) of 98.33% and rice-specific Intersection over Union (IoU_rice) of 83.47%, surpassing single-modality baselines (optical: IoU_rice = 75.78%; SAR: IoU_rice = 66.81%) and alternative fusion approaches. The model exhibits exceptional robustness in cloud-obstructed regions and diverse field patterns, effectively balancing precision (90.98%) and recall (90.35%). These results highlight the superiority of early-stage fusion in preserving complementary spectral–structural information, while revealing limitations of delayed integration strategies. Our work advances multi-modal remote sensing methodologies, offering a scalable framework for operational agricultural monitoring in challenging environments.

Source: Agriculture

DOI: 10.3390/agriculture15070706

Authors: Chenyang He, Jia Song, Huiyao Xu

Institutions: Chinese Academy of Sciences, Institute of Geographic Sciences and Natural Resources Research, University of Chinese Academy of Sciences, State Key Laboratory of Resources and Environmental Information System, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137