IDUDL: Incremental Double Unsupervised Deep Learning Model for Marine Aquaculture SAR Images Segmentation

Abstract

Marine aquaculture is an important natural resource exploration that requires rational planning to avoid environmental damage. Synthetic Aperture Radar (SAR) images are essential in remote sensing to monitor the marine ecological environment. Unsupervised methods provide an adequate solution to avoid the cost of training sample collection. However, unsupervised methods often struggle to discover effective semantic information and incrementally utilize newly acquired data. To address these challenges, this article presents an incremental double unsupervised deep learning (IDUDL) model, which is specially designed to characterize unlabeled marine aquaculture and achieve the results semantically. Based on the idea of alternately generating and updating pseudo-labels, the proposed IDUDL model defines the double neural networks comprised of the feature extraction network (FEN) and the fully convolutional semantic segmentation network (FCSSN). A patch estimation (PE) is proposed to generate pseudo-labels with aquaculture semantic information based on the features extracted by the FEN network. Then, the aquaculture extraction results are obtained by the FCSSN with generated pseudo-labels. After that, the pseudo-labels and extraction results are updated in turns until the pseudo-labels are stable. In addition, due to the unique structure of double neural networks, newly acquired marine aquaculture SAR images can also be added to the pre-trained FCSSN and followed pseudo-labels updated based on the FEN and PE part, which can achieve new data incremental learning without retrained the whole IDUDL model. Experiments demonstrate the effectiveness of the proposed approach based on two different ways of the marine aquaculture including raft and cage types, which consist of GaoFen-3 (GF-3) and RADARSAT-2 SAR images from the Dalian and Ningde areas, respectively. The incremental experiments are also designed to verify the generalization of the IDUDL model for new obtained marine aquaculture SAR images. The code of this work will be available at https://github. com/fjc1575/Marine-Aquaculture/tree/main/IDUDL for the sake of reproducibility.