# Coastal Zone Classification Based on U-Net and Remote Sensing
###### Abstract  
The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model's performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangye Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. a 20  
With the rapid advancement of satellite remote sensing technology, the spatial and spectral resolution of remotely sensed data has significantly improved, greatly enhancing the innovation of information interpretation techniques [12]. In the field of remote sensing data interpretation, traditional human interaction methods have achieved satisfactory classification accuracies ranging from 70% to 90% [13; 14]. However, these approaches are often costly and inefficient [15; 16], and they struggle to handle massive and complex datasets [17]. Researchers have explored the use of machine learning and artificial intelligence to extract coastal zone information, such as the threshold method. However, calculating the appropriate threshold is challenging, especially in areas with low contrast or complex backgrounds, which can significantly affect extraction results [18; 19; 20; 21]. Pixel-based statistical learning methods, including support vector machine (SVM), maximum likelihood classifier (MLC), and minimum distance algorithm (MDA), have been tested for classification. These methods primarily rely on spectral features and require significant spectral differences between ground objects [22; 23; 24]. However, traditional statistical learning methods often overlook spatial and contextual semantic information in remotely sensed data, leading to issues like \"same spectrum with different objects\" and \"same object with different spectra\", which can increase misclassification and omission errors [25]. Furthermore, object-based image analysis (OBIA) methods, as well as those combining OBIA with spectral, shape, texture, and spatial information, have been investigated. However, determining the appropriate scale for OBIA segmentation remains a challenge and is often experience based [26; 27; 28].