## 3 Materials and Methods
### Architecture of U-Net Used for Aircraft Recognition  
As discussed in the previous section, U-Net is introduced as a modified version of the FCN, with two primary architectural distinctions. First, in contrast to FCN, which employs a \(1\times 1\) convolution layer at the last layer of the encoder for utilizing pertained models, U-Net omits this layer from the encoding stage. Second, to enhance the localization accuracy, U-Net leverages high-resolution features by effectively merging the encoding and decoding layers. Originally intended for biomedical segmentation tasks, U-Net has become renowned for its remarkable performance in a variety of image segmentation applications. One of the pivotal features contributing to the efficacy of the U-Net architecture is the incorporation of skip connections. These connections facilitate the fusion of both low-level and higher-level feature maps, a mechanism that plays a crucial role in achieving \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Domain** & **Innovation/Technique** & **Accuracy Achieved** \\ \hline
[31] & Image semantic labeling & Multi-layer perceptron (MLP) was proposed, which outperformed other & 88.92\% \\  & & techniques like CNN & \\
[32] & Aerial image semantic segmentation & Hourglass-shaped network (HSN)-based & 89.42\% \\
[16] & Aircraft classification & Keypoints’ detection network & 95.60\% \\
[17] & Biomedical image segmentation & U-Net architecture was deployed & 92.03\% \\
[33] & Cardiac MR segmentation & U-Net architecture was deployed & — \\
[34] & Satellite image segmentation & U-Net architecture was deployed & 87.61\% \\
[2] & Satellite image segmentation & Improved U-Net architecture & 97.56\% \\
[35] & Forest change detection & U-Net architecture was deployed & 99.00\% \\
[36] & Brain tumor segmentation in magnetic & U-Net architecture was deployed & 99.00\% \\  & resonance imaging (MRI) & U-Net architecture was deployed & 99.00\% \\
[37] & Glaucoma detection in retinal & U-Net and supervised ML algorithms & 100\% \\  & fundus images & ResNet-34 weights within the & 64.00\% \\
[38] & Satellite image segmentation & encoder component & 64.00\% \\
[3] & Remote image segmentation & Contextual U-Net architecture & — \\
[5] & Aircraft image segmentation & Spatial channel attention U-Net model & — \\
[39] & Multi-scale aircraft segmentation & MSRAU-Net & 93.12\% \\
[40] & Aerial drone object segmentation & U-Net segmentation model & 95.00\% \\ This study & Aircraft image segmentation & U-Net segmentation model & 98.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected literature on image detection. precise object localization during the segmentation process. This ability to capture detailed information at multiple levels of abstraction is a key factor behind U-Net's success in producing accurate segmentation results. The architecture employed in this study for performing the semantic segmentation of aircraft satellite images utilizes the U-Net framework, as illustrated in Figure 1. The U-Net architecture comprises two main sections: the left part, referred to as the encoder or contracting path, and the right part, known as the decoder or expansive path. The encoder section follows a conventional CNN structure and consists of four blocks of layers. Each block incorporates two convolutional layers with multiple 3 \(\times\) 3 filters applied sequentially for feature extraction. After each convolution operation, a Rectified Linear Unit (ReLU) is employed as a nonlinear activation function for the feature maps. To achieve down-sampling, max-pooling with a filter size of 2 \(\times\) 2 is applied, reducing the spatial dimensions by a factor of 2. At each step of dimension reduction, the number of channels is doubled, increasing the representational capacity of the network. Both the encoder and decoder sections consist of an equal number of blocks.