Fuzzy Boundary-Aware Network for Hyperspectral Individual Tree Fine Recognition

Different tree species have different carbon storage and growth rates. Therefore, accurate segmentation and identification of individual trees can provide more detailed carbon storage data, which is the basis for accurately estimating forest carbon storage. However, individual tree segmentation and recognition in dense forest areas face challenges such as crown overlap, complex terrain, and species diversity. To address these challenges and improve recognition accuracy, this paper proposes a fuzzy boundary-aware network (FBAN) for hyperspectral individual tree segmentation and recognition in dense forests. The proposed FBAN inclues a boundary-aware module (BAM) that explores channel boundaries between trees and non-trees, spatial boundaries of trees, and spectral boundaries between different trees by intergrating channel attention, spaital attention, and spectral attention. This enhances the separability of individual trees, especially those of the same species that are contiguous in dense forest areas. Additionally, an adaptive crown-aware module (ACAM) is constructed to adapt diverse-size crown features by coupling Transformer layers with dialted convolution layers. Experimental results on different hyperspectral datasets show that the proposed FBAN network outperforms existing methods in dense forest areas and different tree canopy areas, e.g., the AP on the SZU-South dataset is 4.5 points higher than that of Mask2former. It not only improves the accuracy of individual tree segmentation and recognition but also exhibits high generalization and robustness.