Mitigating Incidence Angle Effects in Airborne SAR Time-Series Crop Classification: Integrating Transfer Learning and Variational Mode Decomposition

Abstract

This study introduces a novel approach to improve crop classification accuracy in airborne synthetic aperture radar (SAR) time-series imagery, focusing on overcoming the challenges posed by the incidence angle effect. The approach aims to innovate the integration of transfer learning and variational mode decomposition techniques. Transfer learning effectively addresses disparities in data distribution caused by varying incidence angles encountered in airborne SAR. Variational mode decomposition extracts robust temporal features, significantly reducing sensitivity to incidence angle variations. The approach is further enhanced by incorporating incidence angle information into the transfer learning model's training phase. The experimental results demonstrate the effectiveness of the method, which, under comparable sample conditions, achieves a remarkable improvement in accuracy (Kappa +25.05%) compared with the conventional methods. This improvement is particularly notable for crops, such as oats and soybeans, which are considerably influenced by the incidence angle effect, with Kappa increases of 27.92% and 39.30%, respectively. This study not only develops an effective strategy for crop classification in the context of airborne SAR imagery but also provides references for the effective use of new technologies from various fields in the field of remote sensing application.

Keywords

• Airborne synthetic aperture radar (SAR)
• crop classification
• incidence angle effect
• time-series imagery
• transfer learning
• variational mode decomposition (VMD)