Extraction of aquaculture cages from high-resolution remote sensing images based on deep learning

The accurate recognition of the spatial distribution of aquaculture in coastal areas plays a crucial role in the management of natural resources and marine ecological environment protection. Using remote sensing detection method, the information of aquaculture areas can be quickly and accurately extracted from high-resolution remote sensing images. This work focuses on semantic segmentation and extraction of cage aquaculture regions using advanced deep learning algorithms. We selected Hainan Island in China as the experimental area and established the Hainan Island Offshore Cage Aquaculture Sources Dataset (HIOCASD) using high-resolution satellite remote sensing images from Gaofen-2 satellite. Six different deep learning models including DeepLabv3+, Segformer and U-Net architectures are evaluated with feature extraction via convolutional neural networks. The experimental results show that all models have excellent performance, especially U-Net model which uses VGG network as feature extractor. Through five cross-validations, its average F1 score is as high as 93.75%.