Oceanic Internal Wave Signature Extraction in the Sulu Sea by a Pixel Attention U-Net: PAU-Net
Yuteng Ma, Junmin Meng, Lina Sun, Peng Ren
Abstract
Oceanic internal waves (IWs) are an important oceanic phenomenon, and the realization of the fast and efficient extraction of IWs is of fundamental significance. The development of deep learning techniques provides new opportunities for the signature extraction of oceanic IWs. In this letter, we propose a two-stage oceanic IW signature segmentation algorithm for synthetic aperture radar (SAR) images. The algorithm includes a decision fusion-based oceanic IW images classification stage and a pixel attention U-Net (PAU-Net)-based stripe segmentation stage. First, we adopt an IW classification algorithm by fusing the weak decision results of two different classifiers to get the final strong decision result to screen the image blocks containing oceanic IWs. Then we develop a PAU-Net to segment the IWs stripe. Finally, we concatenate them to obtain the extract results of the whole image. Experiments are performed using 527 image scenes from the Sulu Sea that contain IWs. The results show that the proposed algorithm can achieve the performance of oceanic IW signature extraction from SAR images.