Automatic Extraction of Green Tide From GF-3 SAR Images Based on Feature Selection and Deep Learning

Efficient and accurate monitoring of green tide is of great significance to marine disaster prevention and marine environment protection. A method is proposed in this article for the automatic extraction of the green tide from Chinese Gaofen-3 (GF-3) satellite synthetic aperture radar (SAR) images, which is based on feature selection and deep learning. In this article, since SAR images contain rich polarization information, we first employ H/A/α decomposition and other methods for the extraction of high-dimensional features from GF-3 SAR images. Second, a novel feature selection method for SAR images is designed by using the Bhattacharyya distance and the Separability index, which can select the optimal features subset with a strong ability for recognizing green tide and without correlation between features from the high-dimensional features of SAR. Then, to alleviate the model training burden and improve the prediction efficiency, a lightweight semantic segmentation network, called Mobile-SegNet, is designed based on MobileNets and SegNet. Finally, the selected optimal features and their labels are sent to Mobile-SegNet for training and obtaining the automatic recognition model of green tide, and in turn, the automatic extraction of green tide is achieved through model prediction. To verify the effectiveness of the proposed green tide extraction method, GF-3 SAR remote sensing images taken in 2020 that covered the Yellow Sea are collected and used in the green tide extraction experiment. The results show that the proposed method is available for an effective reduction of the feature dimension required for green tide extraction, and the improvement of the accuracy and efficiency of green tide detection. The overall accuracy, F1-score, mean intersection over union, and the kappa coefficient of the proposed method reached 99.52%, 95.76%, 92.19%, and 0.92, respectively.