# Coastal Zone Classification Based on U-Net and Remote Sensing
## 5 Conclusions  
This study analyzed the LULC classification of a coastal zone using the U-Net method based on GF-2 remote sensing images. The research verified the effectiveness of GLCM texture features, NDVI, and contrast features within the U-Net model, constructing a strategy tailored to high-resolution remote sensing images for coastal area classification. The comprehensive analysis of the experimental results demonstrated the following: (1) The U-Net classification algorithm that combines texture, NDVI, and contrast features significantly improves classification accuracy. (2) The proposed model effectively classifies artificial surfaces, forest land, farm land, and water bodies, achieving high performance in coastal feature classification with an accuracy of 93.65%, making it suitable for practical applications.  
However, there are still some limitations that need to be addressed: (1) The limited spectral bands of GF-2 may not capture all the necessary information for distinguishing various coastal features, particularly those with similar spectral signatures; (2) the U-Net model requires a substantial amount of high-quality annotated training data, which can be labor-intensive and costly to produce; (3) deep learning models trained in one coastal region might not generalize well to other regions due to differing environmental conditions; (4) coastal zones may exhibit imbalanced classes, with some land cover types being underrepresented, leading to biases in the model and inaccuracies in classifying less frequent categories.  
To overcome these shortcomings, future research can focus on the following aspects: (1) Integrating GF-2 data with other high-resolution and hyperspectral datasets to enhance spectral and temporal resolution; (2) exploring hybrid architectures that combine U-Net with other models (e.g., LSTM for temporal data) to improve performance in dynamic environments; (3) utilizing synthetic data generation techniques (e.g., GANs) to create additional training samples, especially for underrepresented classes; and (4) investigating semi-supervised learning approaches to use large amounts of unlabeled data, thereby reducing the dependency on annotated data.  
[PERSON] collected the data and wrote the paper; R.H. and M.Y., review; P.L., review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Hainan Provincial Natural Science Foundation of China (423 MS120), a National Natural Science Foundation of China grant (42071007), a provincial-level project of Hainan Province, Hainan Academy of Marine and Fishery Sciences (KYL-2024-06), a China Scholarship Council grant (201808410212), the Major Science and Technology Plan Project of Yazhou Bay Innovation Research Institute of Hainan Tropical Ocean University (2022 CXYZD003), and the Hebei Provincial Natural Science Foundation of China (D2020409002).  
Not applicable.  
The data presented in this study are available on request from the first author. The data are not publicly available due to [Since GF-2 is neither public data nor commercial data, and a data confidentiality agreement has been signed with the Provincial GF Center, the data cannot be shared at present].  
The authors would like to thank the researchers who provided the open-source algorithms, which were extremely helpful to the research conducted in this paper. We also thank the anonymous reviewers and editors for their contributions.  
The authors declare no conflicts of interest.