A Spectral-Index-Aligned Transfer-Learning Approach for Automated Plantation Segmentation in High-Resolution Remote-Sensing Imagery

Abstract

Plantations play an essential role in regional ecological security and the sustainable use of forest resources, making accurate knowledge of their spatial distribution and management boundaries critical for forest inventories, afforestation assessment, carbon-accounting support, and stand-level management. To address blurred stand boundaries, complex background interference, and regional spectral variation, we propose a plantation segmentation framework termed the Kernel-Guided Forest Segmentation Network (KGF-Net). The method combines a spectral-index aligned fusion module (SIAF), a structure-aware boundary enhancement module (SABE), a cross-domain discriminative geometry-constrained coupling module (CDGC), and an Adam-compatible forest-aware responsive spectral control strategy (FReSCO). In the source-domain stage, KGF-Net is pretrained on the North American subset using aligned RGB, NDVI, and EVI inputs, enabling the model to learn vegetation-sensitive spectral and structural representations. For Asia, Europe, Africa, and public FAIS target datasets, no target-domain vegetation-index layers are used; instead, the source-domain weights are transferred and the model is fine-tuned separately using target-domain RGB images and plantation masks. Thus, the cross-region experiments evaluate source-to-target transfer learning with RGB-based target adaptation rather than zero-shot generalization or independent multimodal training in every region. Experiments show that KGF-Net achieves competitive segmentation and boundary-delineation performance while maintaining a compact computational profile. The resulting maps provide spatially explicit information on plantation extent, patch configuration, and management boundaries, supporting regional plantation monitoring and forest-management applications.