Coastal Zone Classification Based on U-Net and Remote Sensing
Source abstract
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 Shuangyue 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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing and Land Use · Environmental Changes in China · Remote Sensing and LiDAR Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hainan Academy of Ocean and Fisheries Sciences, Haikou 571125, China
Yazhou Bay Innovation Institute, Hainan Tropical Ocean University, Sanya 570100, China
Department of Architecture and Engineering, Baiyin Vocational College of Mining and Metallurgy, Baiyin 730900, China
School of Geography and Environmental Sciences, Hainan Normal University, Haikou 571158, China
Funding
Hainan Provincial Natural Science Foundation of China · National Natural Science Foundation of China · project of Hainan Province, Hainan Academy of Marine and Fishery Sciences · China Scholarship Council Grant · Major Science and Technology Plan Project of Yazhou Bay Innovation Research Institute of Hainan Tropical Ocean University · Hebei Provincial Natural Science Foundation of China
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