# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 1 Introduction
### Related Work and Motivation  
In harbour areas, unplanned high-density farming not only reduces production efficiency but also leads to the deterioration of the marine ecological environment. Therefore, it is important to use remote sensing images to macro-regulate the distribution of cages in the sea [1]. At present, in the field of marculture research, most of the studies do not partition the statistics for cages, but the density of marine organisms such as fish bodies cultured in cages. [PERSON], [PERSON] et al. [2] studied the effect of different culture densities in cages on the growth performance, digestive enzyme activity and non-specific immunityof broad-bodied golden leech for broad-bodied golden leech culture. [PERSON] [3] and [PERSON] [4] studied the effect of different culture densities on the growth of tilapia. However, it is not only the culture density of organisms in cages that can affect the normal growth of fish but also the excessive density of cages in the sea area. Therefore, we need to regulate the distribution of culture cages at a macro level to ensure that their distribution is reasonable. In the field of remote sensing image segmentation, [PERSON] et al. [5] proposed an autonomous improved convolutional neural network mU-ResPlus based on U-Net to address the practical problems of fragmented distribution of small crops and a small amount of data. mU-ResPlus firstly reduced the number of layers of the U-Net network, then used multiple deconvolutions to fuse the features of shallow and deep layers of the image, and introduced the residual blocks to achieve the refinement of the network segmentation effect. The experimental results show that the accuracy and Kappa coefficient of mU-ResPlus are 87.4% and 0.747, respectively, which have high application prospects in the research of small crop image segmentation. [PERSON] et al. [6] proposed a remote sensing image segmentation network MAE-Net combining multi-scale attention and edge supervision, and the experimental results show that MAE-Net could segment buildings completely from remote sensing images with complex and diverse backgrounds and large-scale changes, and the segmentation accuracy is high. [PERSON] et al. [7] improved the DenseASPP network to address the problems of large differences in target scales and blurred boundaries in the semantic segmentation of remote sensing images. The channel attention model and spatial attention model are introduced to give more weight to the key features in the network; the group convolution is used in the sampling of convolution with holes, and the pruning strategy is adopted in the training to discard the connections with lower weights to achieve the sparse network, which effectively overcomes the problem of excessive memory occupation of DenseASPP. Capturing remote dependencies by null convolution or increasing the number of channels would lead to a dimensionality explosion due to the expansion of the model. [PERSON] et al. [8; 9; 10] separately used mask R-CNN, Segnet, and U-Net to segment the remote sensing cage images. The above proposed semantic segmentation networks for remote sensing images all use convolutional neural networks, which can effectively extract local information, but lack the ability to extract remote features from global information. Recently, Transformer-based models have shown prominence in various fields such as natural language processing and computer vision. In computer vision, Vision Transformers [11] (ViTs) have demonstrated optimal performance in various benchmark tests. Specifically, the self-attention module allows for modelling the interaction between token embedding via pairs of remote messages in ViT-based models, leading to more efficient local and global contextual representations. However, it only uses the self-attention mechanism, resulting in much higher image recognition rates than the models based on CNN.