# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
###### Abstract
However, there is a big problem when applying multi-source remote sensing data to forestry research, that is, data fusion; multi-source remote sensing data have different sensors or resolutions, and the data time is not correlated [12]. The diversity and extensiveness of data provide multiple possibilities for the study of forest biomass, but at the same time, it faces more challenges. In 2005, [PERSON] began to use multi-source remote sensing data to invert forest parameters [13]. [PERSON] et al. predicted AGB using airborne laser scanning (ALS) data and TM data collected by the Finnish National Land Survey (NLS), which began in 2008 [14]. [PERSON] et al. used photogrammetric WorldView-2 data, interferometric TANTANDEM X data and hyperspectral EO-1 Hyperion data to estimate the biomass of temperate forest land near Karlsruhe, Germany [15]. Based on multi-source remote sensing data, [PERSON] and [PERSON] et al. adopted the random forest regression algorithm to obtain better results [16; 17]. Based on ALOS PALSAR data and other remote sensing data, respectively, [PERSON] and [PERSON] et al. mainly used a neural network method to estimate biomass, and achieved good inversion accuracy [18; 19]. In recent years, with the rapid development of Deep Learning [20], scholars have applied it to vegetation classification. [PERSON] et al. used SVM to identify 20 types of alkaline grassland vegetation, and the overall classification accuracy reached 82.60% [21]. Based on UAV time series images, [PERSON] et al. used RF and SVM to classify crops grown in Anbandegi, South Korea, and achieved good classification results [22]. [PERSON] et al. compared SVM, RF and ANN classifiers in vegetation classification based on aerial hyperspectral APEX images, and the results showed that ANN classifiers were superior to RF and SVM classifiers [23]. Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition and other fields [24]. [PERSON] et al. used a convolutional neural network (CNN) to identify 11 crops in the study area, and the classification accuracy was 94.6%, which was significantly higher than the RF classification result [25]. [PERSON] et al. used a 3D CNN framework to extract multi-temporal image information for vegetation classification; compared it with SVM, PCA and other classifiers; and found that 3D-CNN can make better use of remote sensing image phase information and improve classification accuracy [26]. In the biomedical image segmentation field, CNN-based encoding-decoding structures have exhibited superior segmentation effects in this field [27]. In the past few years, convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have achieved good results in HSI classification, but CNNs struggle to achieve good accuracy in low samples, while GCNs have a huge computational cost. To resolve these issues, [PERSON] et al. proposed Multi-Feature Fusion of 3D-CNN and Graph Attention Network MFFCG [28]. A neural network is a mathematical model of a distributed parallel information processing algorithm which simulates the behavior characteristics of an animal neural network. Although the combination of different bands is theoretically related to the above-ground biomass of forests, the complex environment of forests makes various statistical functions unable to express all uncertainties, so artificial neural networks have opened up a convenient, fast and reliable method for biomass estimation. This kind of network depends on the complexity of the system by adjusting the interconnection between a large number of nodes in the network to achieve the purpose of processing information. However, the BP (Back Propagation) neural network model has some problems such as slow convergence and local optimization.