Accurate extraction of offshore raft aquaculture areas based on a 3D-CNN model
Zongchen Jiang, Yi Ma
Abstract
Offshore aquaculture plays an important role in China’s marine fishery economy. The research on the extraction of offshore aquaculture areas based on remote sensing technology is of great significance for the regulation of offshore fishery resources and the protection of the marine ecological environment. This paper uses the Gaofen-2 series multispectral remote sensing image to extract the offshore aquaculture areas of Lianyungang City. We use the optimum index factor to extract the spectral features of the aquaculture areas and the grey-level co-occurrence matrix to extract their texture features. We use the Bhattacharyya distance to select the spatial and spectrum features and construct the characteristic data set of the aquaculture areas. In this paper, we propose a method to construct a uniform distributed disturbance term to optimize the cross entropy loss function. We employ it in the three-dimensional convolutional neural network (3D-CNN) model, extract the extended feature data set of aquaculture areas, and input it into the radial basis function support vector machine (RBF-SVM) classifier for classification. Within the study area of 150 km2, the experimental results show that the extraction model has high extraction accuracy and strong spatial migration despite complex water backgrounds. The F1-score values in the training area and the four random test areas were 0.939 or above for 2017 data. In addition, the extraction model also has stable time migration. We used the extraction model on remote sensing data for the same study area in 2018 and 2019. The F1-scores for all test areas are 0.866 or higher. Therefore, the model proposed in this paper is suitable for the extraction of large-scale and multi-temporal offshore raft aquaculture areas from remote sensing images.