# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 2 Related Work  
### Development of DCNNs  
In the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), AlexNet triumphed with an error rate significantly lower than that of other competing models, marking the rise of deep learning in image classification [14]. Subsequently, deeper convolutional neural networks, such as VGG [15], shone brightly in the visual domain. However, in traditional deep neural networks, increasing the depth of the network might lead to issues such as vanishing gradients and exploding gradients, making the training of the network challenging. GoogLeNet introduced the inception block to widen the network, ensuring more comprehensive information extraction and significantly improving classification accuracy [16]. The idea behind the inception block shifted researchers' focus from increasing network depth to achieving better results, although deeper networks remain the primary method of obtaining deep features. Nevertheless, in deeper structures, as information propagates across multiple layers, gradients might diminish over time, leading to minuscule updates in network weights and resulting in a decline in network performance. ResNet effectively addressed this issue with the design of residual blocks [17]. ResNet introduced the concept of residual blocks to tackle the degradation problem in deep neural networks, enabling the network to efficiently learn deep feature representations. This innovation has significantly impacted the successful application of deep learning in fields such as computer vision.  
To address the shortcomings of traditional computer vision methods in pixel-level segmentation tasks, earlier researchers drew inspiration from the characteristics of classification networks. In 2015, [PERSON], [PERSON] et al. first introduced the FCN (fully convolutional network) concept for per-pixel classification tasks [18]. FCN eliminated the fully connected layers found in classification networks, extending the convolutional neural network to handle input images of any size and output pixel-level segmentation results. The introduction of FCN marked a breakthrough in deep learning in semantic segmentation. It realized end-to-end feature learning and segmentation, greatly simplifying the entire process. FCN has been widely applied in various domains, including medical image segmentation, autonomous driving, and satellite image analysis [19].  
End-to-end semantic segmentation networks have been widely adopted, and these methods have evolved from the foundational FCN. This includes prominent networks such as SegNet [20], UNet [21], the Deeplab [22; 23] series, and PSPNet [24]. SegNet introduced the encoder-decoder structure and employed skip connections and hierarchical softmax for pixel classification, aiming to retain more detailed information. UNet, with its encoder-decoder and skip connection architecture, improved classification accuracy when applied to small datasets, leading to its widespread use in scientific research. Subsequent variations, including UNet++ and others, have also become classic models in semantic segmentation. However, the UNet series is not without its limitations. As training iterations increase, the network may experience degradation, and UNet struggles to achieve satisfactory results when segmenting complex categories. Addressing the challenges of complex segmentation categories, both DeeplabV3+ and PSPNet introduced a pyramid structure to handle features of different scales. This design aims to capture contextual information from various scales, better addressing different object sizes and details within images. In summary, these networks, primarily designed for semantic segmentation, have been widely recognized and accepted across various domains.