# DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images
## 4 Discussion
### Dig Deep Reason for the Results  
The SVM method has a statistically significant advantage where the segmentation size does not need to be considered when pixel-based reflectance samples are used. [PERSON] and [PERSON] [46; 47] have claimed that the support vector machine method could be used to deal with complex classification problems, such as tree species classification, wherein they classified five tree species using SVM with Hyperion data. In the present paper, mongolica pine, spruce, willow, and poplar were almost identified as larch. Therefore, SVM has serious defects in the recognition ability of coniferous forest species. [PERSON] [48] significantly recognized eight dominant tree species, the OA of which reached 0.82 compared to the SVM classifiers with 0.52. The views that were obtained from the compared DL models are basically consistent with the study conducted by [PERSON], as shown in Figure 9. The CD-CNN method exploited the local spatial-spectral relationships neighboring each pixel vector to explore local contextual interactions, however, which hardly recognize any Poplar, in contrast, DBMF have a good effect on the extraction of Poplar. Because CD-CNN is based on 2D-CNN, it has certain shortcomings for complex multi-tree species classification with samples that have small sizes. However, the 3D-CNN method is more lightweight, general, and fast-converging, wherein the convolution operation can retain finer spectral information and provide a good classification model structure for tree classification works where it is difficult to obtain huge samples [17]. Therefore, this method cannot satisfy the request in tree species classification. The DBMA method based on 3D-CNN employs the channel-wise attention and spatial-wise mechanism to enhance features, although the training process is more time-consuming than CD-CNN because of the parameters, whereas DBMF fails to consider the order and relationship of the HS data. Therefore, the two shortcomings above make DBMA more time-consuming and perform worse compared to the DBMF method for tree classification [43]. Compared with DBMA, DBDA adds the Mish activation function to extract the information of some negative parameters while simultaneously increasing the complexity of the algorithm and slowing down the efficiency in the training process. This makes the tree species map more accurate than DBMA in classifying tasks, but at a cost of it performing worse than DBMA. Because the proposed DBMF method can be improved in terms of training speed by employing sandbox so that it does not lose the extraction of effective information ability and the connection with the spatial-spectral feature, it does not pre-process or post-process data and the spatial and spectral information of HSIs so that they can be fully utilized to achieve the desired classification accuracy. It solves the problem of information loss caused by feature downscaling and feature screening of the original spectral-spatial information, and therefore DBMF can be applied in the field of forestry scientific management (Objective 3).