# Coastal Zone Classification Based on U-Net and Remote Sensing
###### Abstract
In recent years, deep learning, a key component of artificial intelligence, has played a crucial role in the advancement of image classification. Deep learning techniques offer several advantages in remote sensing image classification, including the ability to automatically adjust model parameters, generate models with strong generalization capabilities, improve accuracy, handle large and complex datasets, perform automatic feature extraction, and learn from multi-source and multi-temporal data [29; 30; 31; 32]. For instance, [PERSON] et al. [33] discussed the state of the art in deep learning for remote sensing and highlighted the significant improvements in classification accuracy achieved by these methods. [PERSON] et al. [34] reviewed deep learning applications in remote sensing and emphasized their ability to manage large and complex datasets. They illustrated how convolutional neural networks (CNNs) can automatically extract relevant features from hyperspectral images, eliminating the need for manual feature engineering [35]. Popular deep network architectures include CNN [36], AlexNet [37], VGG [38], GoogLeNet [39], and ResNet [40]. CNNs are foundational for semantic segmentation in deep learning, typically generating several fully connected layers with multiple convolutional layers. They map high-dimensional feature images into N-dimensional feature vectors to predict the probability of each pixel belonging to a specific category [41]. To address the limitations of CNNs, such as high computational cost and limited ability to capture contextual information, [PERSON] et al. [42] designed a fully convolutional network (FCN) in 2015. FCN replaces the fully connected layers in CNNs with deconvolution layers, creating an end-to-end network structure that produces segmentation results with the same resolution as the input image, thus enabling accurate image classification [43; 44]. Building on FCN, [PERSON] et al. [45] proposed an end-to-end multi-layer fusion fully convolutional neural network, which can fuse deep features of images obtained from different sensory field sizes. However, FCNs also have some limitations, including high computation cost [46], limited ability to capture contextual information [47], challenges in handling multi-scale objects [48], tendency to overfit training data [49], reliance on extensive labeled datasets for training [50], and sensitivity to noise and variability in remote sensing images, which may impact classification accuracy [51]. To overcome these challenges, [PERSON] et al. [52] extended the FCN and introduced a new symmetric network called U-Net. In the U-Net structure, the encoding part on the left adopts the VGG-Net network, while the decoding part on the right is cascaded with the left part. This structure allows for the combination of low-level features obtained through down-sampling with high-level features obtained through up-sampling, accurately locating pixel information. The U-Net not only produces classification results with the same resolution as the input image but also ensures that the target objects have well-defined contour information [53]. This paper aimed to (1) develop a model with high accuracy for coastal zone classification using GF-2 remote sensing images, (2) evaluate the effectiveness of texture, Normalized Difference Vegetation Index (NDVI), and contrast features in the U-Net model, and (3) propose a LULC classification solution suitable for coastal areas, constructing a strategy based on high-resolution remote sensing images. To achieve these goals, we first generated an LULC map using the original multispectral GF-2 imagery and the U-Net model. Then, the classification results were compared with those obtained from mainstream deep learning architectures such as SegNet [54] and DeepLab v3+ [55; 56; 57; 58], as well as state-of-the-art statistical classifiers like SVM and RF. Next, we constructed different feature spaces using Gray Level Co-occurrence Matrix (GLCM) texture [59; 60], Histogram Contrast (HC) [61], and NDVI index [62].