## 2 Background and Related Literature
### CNNs for Semantic Segmentation of Remote Sensing Images  
Deep neural networks, particularly CNNs, have gained considerable attention in the large-scale processing of remote sensing images in recent years. These networks have proven to be the top-performing tools for high-resolution semantic labeling and have been applied in various remote sensing tasks. For instance, in [31], experiments were conducted using high-resolution remote sensing imagery to analyze several dense semantic labeling CNNs by developing a multi-layer perceptron CNN model. In another work presented in [32], a network architecture with an hourglass shape was designed, complemented by a down-sampled network followed by an up-sampled network. It introduced an inception module, spanning from the encoder to the decoder, facilitating the direct flow of information across different network layers. This design choice aimed to enhance the network's ability to capture and utilize information effectively during the semantic labeling process. In [16], deep CNNs were offered as a framework for classifying different types of aircraft. The method was a multi-step procedure. First, a specialized network was created to segment airplanes, yielding complex findings that captured the specifics needed to differentiate between various aircraft types. The segmentation outcomes were subsequently improved by adding a network for keypoint detection to recognize aircraft bounding boxes and orientations. The IoU measure was used to evaluate the similarity between segmentation outputs and specified templates, hence confirming the findings. Finally, a template matching approach was utilized for aircraft recognition. While such CNN models are typically resilient to label noise, it is vital to remember that they are in fact data hungry. When given a significant amount of training instances, frequently ranging from hundreds to millions or even billions, they tend to function at their best. This emphasizes the need to have a substantial and rich training dataset to enable the efficient training and generalization of CNN-based models for image segmentation tasks. The U-Net architecture [17] represents a specific type of FCN that has gained significant attention for its effectiveness in segmenting both 2D and 3D biomedical images [17; 33]. Subsequently, it was recognized that this model also performs exceptionally well in the context of pixel-wise satellite image classification. Recently, U-Net achieved great performance not only for images related to the biomedical processing field but also for object segmentation from satellite images [2; 34; 35; 36; 37]. In the study conducted in [38], an approach was applied to address the challenge of semantic segmentation in satellite images. They devised an architecture akin to U-Net, leveraging ResNet-34 weights within the encoder component. This algorithm demonstrated impressive results in the detection of roads from satellite images sourced from the DeepGlobe database, achieving a notable public score of 0.64. To facilitate the semantic segmentation of remote sensing images, the authors in [3] introduced a contextual U-Net architecture. Within this framework, they integrated three collaborative enhancements: a module designed to extract boundary features, enabling the fusion of both semantic and adaptive characteristics; an adaptive feature selection module to prioritize important semantic channels, especially for handling irregular objects; and, to effectively combine hierarchical features while utilizing dynamic inter-layer feature guidance, they incorporated a recursive feature fusion module. To detect edge maps in optical remote sensing images, the authors of [5] created a spatial channel attention U-Net model. This model effectively highlights aircraft within the images. The spatial channel attention U-Net provides significant edge cues, while the usage of encoders ensures a robust representation of salient object features. Subsequently, the decoders receive the output from the encoders.