## 1 Introduction  
Forest type classification is of fundamental importance for sustainable forest management, i.e., biodiversity modelling [1], management of disturbances [2; 3] and fires [4], harvesting [5], biomass evaluation [6], and carbon stock calculations [7]. Unlike urban area classification, forest type classification is challenging given the limitations in training images and ground truth. Expert knowledge is required to prepare training images for convolutional neural networks (CNNs), but there are few open-source datasets for forests [8]. Mountainous terrain is more susceptible to spectral reflectance distortion and canopy shadowing than flat land [9] and has high biodiversity [10]. Moreover, the vegetation is heterogeneous [11; 12; 13] because of the effects of climatic and regional parameters [14; 15]. In the 1930s, before the advent of aerial photography, forest managers collected information using ground inventories [16]. The classification of medium-resolution satellite images has been performed since the 1970s [17]. Natural RGB photographs have been used to identify forest tree species [18] and to classify forest types [19]. Although extensive calibration and pre-processing are not required [20], traditional forest mapping and monitoring requires expensive, time-consuming, and potentially unreliable field measurements, along with professional expertise for manual interpretation [19]. Forest labels are assigned to aerial photographs by humans who evaluate textures, shapes, colours, sizes, patterns and associations [21], vegetation composition, forest structural properties (i.e., stem density, tree size, and vertical structure), and surface morphology [22]. Automated approaches for forest type mapping with remote sensing data can be categorised into three types: traditional thresholding methods, classical machine learning methods, and CNN methods. Traditional thresholding methods are limited to colour features (hue, chroma, and lightness) [23; 24] and the discrimination of under-story (background) and over-story (tree canopy) signals in forests, given the subtle changes in complicated illumination environments. Classical machine learning methods, i.e., support vector machine (SVM) [25] and random forest (RF) [26], handle classification tasks [27; 28; 29; 30; 31; 32] using a small training dataset with Gabor, Canny, Prewitt, Robert, and Gaussian filters applied to aid classification [33; 34]. Recently developed CNN methods are effective for representing spatial patterns, such as edges, corners, textures, and abstract shapes, but CNN-based forest type classification with remote sensing data is still a very new field [35]. Most deep learning models are based on classical CNN models (e.g., U-Net [36] and DeepLabv3+ [37]) for mapping non-forest [38; 39], forest [40] or tree species [18; 37]. U-Net [41] can obtain underlying spatial features using contraction and expansion blocks throughout the encoder and decoder, thereby effectively expanding the horizon of the sensing fields to assess the global contextual environment and to derive detailed information. However, U-Net extracts high-level semantic information using a filter of a certain size and continuously feeds the features to convolutional layers, while ignoring the large amount of low-level information and ignore the correlations between adjacent pixels [42; 43; 44; 45; 46; 47; 48]. Redundant information is used many times during the processing flow, which reduces the capacity to represent key features. The skip connection of U-Net++ [49] helps prevent the potential loss of information caused by sampling [36]. However, the number of operation parameters is very large given the dense skip connections [50], which reduces model efficiency and increases the computational load. Also, during feature fusion, upper-layer semantic information is ignored and the network cannot yield effective fine-grained features at the decoding stage, resulting in serious loss of edge and positional information [51].