## 1 Introduction
Principle component analysis (PCA) [52] and vision transformer (ViT) [53] were proposed for extracting minimal features. Research has also focused on using critical local image patches and discarding useless information [47; 48; 54]. Graph convolutional neural networks (GCNs) have recently been used successfully to analyse irregular data. Graphical spectral, spatial, and geometric data are rich in node and edge information [55; 56]. In a previous study, a GCN [57] performed very well when extracting features from irregular graphical data and edge connections to aggregate node information and generate new representations of the nodes. A GCN is limited to shallow layers because of the vanishing gradient. To extract more features during semantic segmentation, several studies [58; 59; 60] combined CNNs with GCNs to strengthen the spatial and spectral information and to reduce pixel-level noise by identifying graphical nodes and the spatial relationships between them, as represented by graphical edges. [PERSON] et al. [61] collaborate the Euclidean data-oriented CNN with non-Euclidean data-oriented GCN in a single network to generate complementary spectral-spatial features from the pixel and superpixel levels respectively. [PERSON] et al. [62] and [PERSON] et al. [63] fused two global feature vectors produced by individual CNNs and GCNs; the fused features were optimised. However, robustness may have been low. [PERSON] et al. [64] fused features using MopNet and the parameters of a CNN and GCN; all features were continuously updated via backpropagation guided by joint loss. Japan has a typical marine climate with abundant rainfall that facilitates forest growth. Nearly 70% of Japan is covered by forests, of which 40% are plantations [65]. Natural mixed forest (NMX) is defined as forest in which broadleaved species account for 25-75% of the coniferous canopies [66]. Plantation and natural forests differ at the leaf scale (i.e., in leaf inclination, morphology, and clumping) [9] and canopy structure scale (i.e., in crown morphology and canopy cover) [67], as well as in spectral characteristics [68; 69; 70; 71]. They also differ at the forest stand scale (i.e., in forest composition and diversity and tree distributions and interactions within the forest). It is challenging to segment NMX, natural broadleaved forest (NBL), and conifer plantation (CP) because a mixed forest is more heterogeneous than other forests; it contains both conifers and broadleaved trees. In terms of vertical structure, unlike an NBL with a single-story stand, an NMX has multistorey classes with varying vertical structures within each stand type [72]. Furthermore, the natural forests of Japan are mostly located on steep mountains, which makes forest inventory taking and monitoring difficult [19]. Challenges to achieving highly accurate forest type classification include the complexity of natural forest canopy [73], which exhibits multiscale differences [9]. We can easily identify vegetation properties of interest, even with little reference data, in remote sensing data due to the distinct canopy structure or contrasting flowers, while subtle differences [18] and complex relationships require complex algorithms and more ground truth samples to identify specific features [74]. Second, the ambiguity of the boundaries of different forest types [9], which usually have a high chance of land-cover mixing [11; 26; 30], affect the edge pixels. Finally, redundant content and noise in very high spatial-resolution remote sensing datasets [25] (such as crown shadow effects [75]) and continuous CNN filters [54] lead to intraclass variation and high interclass similarity. Our main aims were to develop a novel U-shaped deep learning method using a multiscale global graph convolutional neural network (MSG-GCN) to improve forest type classification accuracy. This network has several notable features.