Multisource Remote Sensing Fusion for Marine Aquaculture Information Extraction

Floating raft aquaculture is an important part of mariculture, and the use of single source data is easy to inhibits the extraction of floating raft aquaculture, so the advantages of integrating multisource data are particularly important. However, multisource remote sensing fusion still has the problem of data information imbalance and feature redundancy. In this paper, a channel exchanging bottleneck attention network (CEBANet) is proposed to extract features from multisource remote sensing data, determine whether features are redundant by using the scaling factor of batch normalization (BN) layer, replace the current redundant features with another modal feature. Bottleneck attention module (BAM) and feature calibration module are added to improve the capability of feature extraction and receptive field processing. The CEBANet is optimized by the sparse constraint on channel exchanging condition and cross-entropy loss and consistency loss. Using GF-5 and GF-3 data from Jinzhou District of Dalian City, it is proved that the proposed model can realize the complementary advantages of multisource remote sensing data and improve the accuracy of extracting floating raft aquaculture areas.