# Aerial Image Segmentation of Nematode-Affected Pine Trees with U-Net Convolutional Neural Network
## 2 Materials and Methods
### Methods
#### 2.2.2 U-Net Construction  
In semantic segmentation tasks, the receptive field size is paramount for achieving precise segmentation outcomes. Existing techniques, including the Atrous Spatial Pyramid Pooling (ASPP) model [29; 30], Pyramid Pooling Model [31], and the employment of large convolutional kernels [32], strive to enlarge the receptive field. However, these approaches often involve a substantial computational cost for determining model parameters, consuming significant memory and consequently leading to slower training speeds. To tackle this challenge, this paper leverages VGG as the backbone network and integrates it with the U-Net architecture. The depth of the U-Net network enables the extraction of high-dimensional features from the input image. By bridging corresponding layers between the encoder and decoder paths, the proposed method effectively fuses low-level features with high-level features, ultimately enhancing segmentation accuracy.  
Using VGG has several advantages. Firstly, the number of parameters in the multi-layer small convolution kernel is significantly smaller than that of a large convolution kernel. Secondly, the multi-layer small convolution kernel performs nonlinear activation only once, followed by multiple linear combinations and nonlinear activations, thereby capturing  
Figure 4: UAV images and corresponding labels.  
higher-order functions and enhancing the expressive ability of the network. For example, a 3 \(\times\) 3 convolutional kernel commonly used in VGG is equivalent to performing two convolutions with a 5 \(\times\) 5 convolutional kernel while reducing the number of parameters to 72% of the original size. This comparison highlights the efficiency of the 3 \(\times\) 3 convolutional kernel in terms of parameter reduction while convolving on the same image.  
By leveraging the fusion of VGG and U-Net, this proposed method achieves an effective combination of low-level and high-level features, leading to improved segmentation accuracy without the need for computationally intensive operations or excessive memory consumption.  
The feature information extracted from the VGG backbone network is fused with the up-sampled feature information to obtain more profound feature insights. Unlike the conventional U-Net structure, this approach involves increasing the number of layers in the U-Net to extract higher-dimensional features. The left portion of the network, representing the encoder, utilizes the VGG backbone network to extract input image features. VGG16 comprises a total of 16 layers structured into five convolutional blocks, each containing three microstructures. Within these microstructures, 3 \(\times\) 3 small convolutional kernels are chosen due to their superior fitting effect and lower computational complexity.  
On the other hand, the right portion of the network acts as the decoder and consists of the up-sampling module and skip connections. The up-sampling module expands the input tensor by a factor of two through up-sampling operations and connects the feature tensor from the previous encoder layer to the up-sampled result. A series of convolution and ReLU activation function operations are then applied to generate the final output.  
In the U-Net architecture, the concatenation splicing layer plays a crucial role by connecting the encoder feature tensor in each up-sampling module to the corresponding higher-resolution feature. This layer maintains spatial information, thereby enhancing segmentation accuracy, and facilitates faster learning of mapping relationships between features. Additionally, U-Net employs skip connections to pass encoder features directly to the decoder. This strategy enables the decoder to leverage semantic information from different levels, ultimately improving segmentation performance. By facilitating the direct transfer of underlying feature information to the decoder, skip connections help alleviate information loss issues. The U-Net structure of this paper is in Figure 5.  
Figure 5: U-Net structure.