Sentinel-1/2 Image Fusion Coupled With CSR, GAN, and Temporal Phenology Feature Construction for Cropland Mapping

Synthetic aperture radar (SAR) and optical image fusion can leverage the strength of both data sources for improved ground object identification. However, existing fusion methods often encounter challenges such as spatial information loss and spectral distortion, and few studies address scenarios where fused images struggle to distinguish between distinct ground objects simultaneously, such as crops at different growth stages and low woodland. To address these challenges, we propose a hybrid fusion method for Sentinel-1/2 images and construct a temporal phenology feature for improved crop mapping. To efficiently preserve spatial and spectral information, our method combines principal component analysis (PCA) transform, and utilizes convolution sparse representation (CSR) and generative adversarial network (GAN) to fuse the high- and low-frequency coefficients of nonsubsampled shearlet transform (NSST), respectively. Furthermore, leveraging the annual normalized difference vegetation index (NDVI) curve to capture the crop growth cycle, we analyze ground object change patterns across a growth cycle and construct temporal phenology feature based on fused images to enhance crop classification accuracy. Results demonstrate that our method outperforms six classical methods and five state-of-the-art deep learning methods across both subjective and objective evaluation performance. When compared with independent SAR, optical, and fused images, the combination of fused images and temporal phenology feature improves the distinction between cropland and woodland and achieves the highest classification accuracy. We also expand the application scenarios to a total of twelve agricultural regions in China, Canada, the United States and France, yielding satisfactory results.