# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 1 Introduction
### Deep Learning Algorithm  
Deep learning is a research subfield in machine learning that aims to build abstract hierarchical models within datasets, employing an incremental approach. Inspired by the deep structure function of the human brain, DL algorithms activate the effective information by many layers of nonlinear transformation operations to compose a learning model to build a mapping relationship between input data and output data [20]. Recently, the DL method has exhibited success for tree species detection [21], crop classification [22], and HSI classification [23]. Deep learning has demonstrated superior results over other commonly used classifiers for trees recognition [24; 25]. [PERSON] et al. [11] classified four tree species by employing random forest (RF) classifier with MSI data captured by a rotating sensor on an unmanned aerial vehicle (UAV) in a broadleaf forest, and the overall accuracy (OA) of classification results reached 78%. [PERSON] et al. [26] applied a three-dimensional convolutional neural network (3D-CNN) to recognize three main tree species with HS data from a boreal forest in Finland, and the OA reached 0.96 on the validation dataset by applying the proposed CNN model. Contextual interactions were obtained by exploiting the local spatial-spectral relationship of adjacent pixel vectors in a square window in the contextual deep CNN (CD-CNN) [27]. [PERSON] [28] proposed the long short-term memory (LSTM) model based on frequency band grouping and a multi-scale CNN as spectral and spatial feature extractors, respectively. The dual CNN branch structure proposed by [PERSON] [29] can be used to extract spectral features from low-resolution HSIs and spatial neighborhood feature information from high-resolution MSIs.  
There is an abundance of remote sensing data available concerning the classification of tree species, but they have not been deeply mined for potential information [30]. [PERSON] and [PERSON] [31] identified the tree species and estimated stock volume with the VGG16 and UNET model. Because there is no better classification strategy to solve the problem of the accurate classification of regional tree species, it is not yet possible to further improve the fusion classification strategy. In recent years, Sentinel-2 satellite MS data have been applied in tree species classification technologies because of their low cost and HR [6]. With the DL method, it is possible to improve the existing classification strategy by combining the data of two satellites and extracting feature information for tree species recognition [25]. Although existing tree species classification network designs are relatively sophisticated, they are rarely used with multi-source networks due to their complexity or relatively low accuracy. Firstly, these DL networks can capture finer features, although the training work will be more difficult and very time-consuming and resource-intensive. Secondly, the remaining representation is compressed and connecting identity mappings between thin bottlenecks will inevitably lead to information loss. In addition, due to feature dimensionality reduction, gradient confusion also weakens the ability of gradients to propagate between layers, affecting the training convergence and model performance. Only efficient feature selection and deeper depth mining can eliminate the redundancy to retain the depth features and ensure that the tree species are correctly classified. When the spatial and spectral block are adjusted, this type of classic DL structure can be applied with multi-source networks, yielding state-of-the-art results. Thus, designing more efficient network architectures is essential for yielding efficient models [32].