Feature-Level Fusion between Gaofen-5 and Sentinel-1A Data for Tea Plantation Mapping

Abstract

The accurate mapping of tea plantations is significant for government decision-making and environmental protection of tea-producing regions. Hyperspectral and Synthetic Aperture Radar (SAR) data have recently been widely used in land cover classification, but effective integration of these data for tea plantation mapping requires further study. This study developed a new feature-level image fusion method called LPPSubFus that combines locality preserving projection and subspace fusion (SubFus) to map tea plantations. Based on hyperspectral and SAR data, we first extracted spectral indexes, textures, and backscattering information. Second, this study applied LPPSubFus to tea plantation mapping with different classification algorithms. Finally, we compared the performance of LPPSubFus, SubFus, and pixel-level image fusion in tea plantation mapping. Feature-level image fusion performed better than pixel-level image fusion. An improvement of about 3% was achieved using feature-level image fusion compared to hyperspectral data alone. Regarding feature-level image fusion, LPPSubFus improved the overall accuracy by more than 3% compared to SubFus. In particular, LPPSubFus using neural network algorithms achieved the highest overall accuracy (95%) and over 90% producer and user accuracy for tea plantations and forests. In addition, LPPSubFus was more compatible with different classification algorithms than SubFus. Based on these findings, it is concluded that LPPSubFus has better and more stable performance in tea plantation mapping than pixel-level image fusion and SubFus. This study demonstrates the potential of integrating hyperspectral and SAR data via LPPSubFus for mapping tea plantations. Our work offers a promising tea plantation mapping method and contributes to the understanding of hyperspectral and SAR data fusion.