Water-Body Detection From Spaceborne SAR Images With DBO-CNN

In recent years, the application of deep learning for water-body detection in synthetic aperture radar (SAR) images has seen extensive development. However, a significant proportion of these works primarily concentrate on enhancing and optimizing the model structure, with inadequate exploration of the potential impact of hyperparameter settings, a critical determinant of model performance. Thus, to fully exploit the power of deep learning in water-body detection from SAR images, this letter presents a diversified optimization strategy that revolves around the dung beetle optimizer-convolutional neural network (DBO-CNN) model, complemented by characteristic fusion and decision-level fusion. The DBO-CNN model employs the DBO algorithm to search for the optimal hyperparameter of the CNN model for bolstering the performance of water-body detection in SAR images. To further enhance the performance, the DBO-CNN model uses unique input data which is constructed by integrating the polarimetric characteristic obtained from $H/\alpha $ and model-based (MB) polarization decomposition methods with backscatter characteristics. Finally, two decision-level fusion methods are proposed to optimize detection results, enhancing the recall and intersection over union (IoU) to 96.5% and 91.5%, respectively. In summary, Spaceborne SAR images, with the application of polarization decomposition and neural network, provide new insights and in-depth understanding for detecting water-body.