# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 1 Introduction  
Mangrove forests are one of the typical marine ecosystem landscapes, which are distributed in tropical and subtropical coastal areas. They are also known as the \"green lung of the ocean\", as they provide various ecological and economic benefits, such as purifying seawater, storing carbon, maintaining biodiversity, and protecting the coastline from erosion and storms. In recent years, positive progress has been made in protecting and restoring mangroves around the world, as more people have recognized their value and importance. However, mangroves are still facing many threats, such as deforestation, urbanization, pollution, and climate change. These threats can lead to habitat degradation and biodiversity reduction, which can affect the functions and services of mangroves. Therefore, it is necessary to enhance the monitoring capabilities and produce more detailed maps of mangroves, which can help the management and protection of mangroves [1]. The maps can provide useful information for assessing the status and trends of mangroves and designing appropriate strategies for sustainable management of mangroves. In China, mangroves are mainly distributed in Guangdong, Guangxi, Hainan, Fujian, and Zhejiang provinces, and they are mainly located in the tidal flat area [2]. Compared to other terrestrial ecosystems, field surveys in mangrove areas are more time-consuming and laborious [3]. Remote sensing is an ideal choice for quantitative monitoring of the distribution and extent of mangroves due to its advantages of wide coverage and high effectiveness [4]. However, mangrove monitoring capability is influenced by the extraction method and the resolution of satellite sensors [5]. At present, classification methods commonly used for mangrove mapping from satellite images mainly include pixel-basedand object-oriented classifications [6; 7; 8; 9; 10]. [PERSON] et al. [6] used IKONOS image and corresponding texture information to classify mangroves based on pixel maximum likelihood classification (MLC), with an overall accuracy of 82%. [PERSON] et al. [7] used Pleiades-1 satellite images and the Random Forest (RF) algorithm to classify mangroves in the Nansha wetland, and the overall accuracy was 82.4%. However, traditional pixel-based classification has a \"pepper and salt\" problem, which shows a discrete and random distribution of white or black pixels in an image. Object-oriented classification uses both spectral characteristics and texture information of mangroves, avoiding the \"pepper and salt\" problem. [PERSON] et al. [8] used an object-based logistics model tree (LMT) algorithm to map the distribution of mangroves from 2010 to 2015 based on ALOS-2 PALSAR imagery, and they obtained an overall accuracy of 83.8%. In recent years, deep learning methods have been widely applied to coastal and offshore environmental monitoring such as waterline extraction [11], flood mapping [12], sea subsurface temperature reconstruction [13], etc. With the development of deep learning in the field of image recognition, the application of deep learning in mangrove monitoring has also become a research hotspot. [PERSON] et al. [14] proposed a deep convolutional neural network (CNN) that automatically detects mangroves over Fidji in the South Pacific Ocean from Sentinel-2 and World-View 2 images with resolutions of 10 m and 50 m, respectively. [PERSON] et al. [15] applied the LeNet-5 network to extract mangrove species information from unmanned aerial vehicle (UAV) images, resulting in an overall recognition rate of 87.31%. [PERSON] et al.